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Repository: GokuMohandas/madewithml
Branch: main
Commit: 3361aeb8ddfc
Files: 56
Total size: 3.0 MB
Directory structure:
gitextract_lbngfpom/
├── .github/
│ └── workflows/
│ ├── documentation.yaml
│ ├── json_to_md.py
│ ├── serve.yaml
│ └── workloads.yaml
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE
├── Makefile
├── README.md
├── datasets/
│ ├── dataset.csv
│ ├── holdout.csv
│ ├── projects.csv
│ └── tags.csv
├── deploy/
│ ├── cluster_compute.yaml
│ ├── cluster_env.yaml
│ ├── jobs/
│ │ ├── workloads.sh
│ │ └── workloads.yaml
│ └── services/
│ ├── serve_model.py
│ └── serve_model.yaml
├── docs/
│ ├── index.md
│ └── madewithml/
│ ├── data.md
│ ├── evaluate.md
│ ├── models.md
│ ├── predict.md
│ ├── serve.md
│ ├── train.md
│ ├── tune.md
│ └── utils.md
├── madewithml/
│ ├── __init__.py
│ ├── config.py
│ ├── data.py
│ ├── evaluate.py
│ ├── models.py
│ ├── predict.py
│ ├── serve.py
│ ├── train.py
│ ├── tune.py
│ └── utils.py
├── mkdocs.yml
├── notebooks/
│ ├── benchmarks.ipynb
│ ├── clear_cell_nums.py
│ └── madewithml.ipynb
├── pyproject.toml
├── requirements.txt
└── tests/
├── code/
│ ├── conftest.py
│ ├── test_data.py
│ ├── test_predict.py
│ ├── test_train.py
│ ├── test_tune.py
│ ├── test_utils.py
│ └── utils.py
├── data/
│ ├── conftest.py
│ └── test_dataset.py
└── model/
├── conftest.py
├── test_behavioral.py
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/documentation.yaml
================================================
name: documentation
on:
push:
branches:
- main
jobs:
build-docs:
runs-on: ubuntu-22.04
steps:
# Set up dependencies
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.10.11'
cache: 'pip'
- run: python3 -m pip install mkdocs==1.4.2 mkdocstrings==0.21.2 "mkdocstrings[python]>=0.18"
# Deploy docs
- name: Deploy documentation
run: mkdocs gh-deploy --force
================================================
FILE: .github/workflows/json_to_md.py
================================================
import json
import sys
def to_markdown(data):
markdown = ""
for key, value in data.items():
markdown += f"**{key}:**\n\n"
if isinstance(value, dict):
markdown += "| Key | Value |\n| --- | --- |\n"
for nested_key, nested_value in value.items():
nested_value = (
round(nested_value, 3)
if isinstance(nested_value, float)
else {k: round(v, 3) for k, v in nested_value.items()}
if isinstance(nested_value, dict)
else nested_value
)
markdown += f"| {nested_key} | {nested_value} |\n"
elif isinstance(value, list) and all(isinstance(item, dict) for item in value):
if value:
headers = sorted(set().union(*[item.keys() for item in value]))
markdown += "| " + " | ".join(headers) + " |\n| " + " | ".join(["---"] * len(headers)) + " |\n"
for item in value:
value_list = [
"{:.3e}".format(float(item.get(header, ""))) if not str(item.get(header, "")).isdigit() else str(item.get(header, ""))
for header in headers
]
markdown += "| " + " | ".join(value_list) + " |\n"
else:
markdown += "(empty list)\n"
else:
markdown += f"{value}\n"
markdown += "\n"
return markdown
def json_to_markdown(json_fp, md_fp):
"""Convert a json file to markdown."""
# Read JSON file
with open(json_fp, "r") as file:
data = json.load(file)
# Convert to markdown
markdown = to_markdown(data)
# Save to markdown file
with open(md_fp, "w") as file:
file.write(markdown)
return markdown
if __name__ == "__main__":
# Check if the correct number of arguments is provided
if len(sys.argv) < 3:
print("Usage: python script.py <json_file> <output_file>")
sys.exit(1)
# Get the JSON file path and output Markdown file path from command-line arguments
json_file = sys.argv[1]
md_file = sys.argv[2]
# Call the JSON to Markdown conversion function
json_to_markdown(json_file, md_file)
================================================
FILE: .github/workflows/serve.yaml
================================================
name: serve
on:
workflow_dispatch: # manual
push:
branches:
- main
permissions: write-all
jobs:
serve:
runs-on: ubuntu-22.04
steps:
# Configure AWS credentials
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v2
with:
role-to-assume: arn:aws:iam::593241322649:role/github-actions-madewithml
role-session-name: s3access
aws-region: us-west-2
# Set up dependencies
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.10.11'
cache: 'pip'
- run: python3 -m pip install anyscale==0.5.131 typer==0.9.0
# Serve model
- name: Serve model
run: |
export ANYSCALE_HOST=${{ secrets.ANYSCALE_HOST }}
export ANYSCALE_CLI_TOKEN=${{ secrets.ANYSCALE_CLI_TOKEN }}
anyscale service rollout --service-config-file deploy/services/serve_model.yaml
================================================
FILE: .github/workflows/workloads.yaml
================================================
name: workloads
on:
workflow_dispatch: # manual
pull_request:
branches:
- main
permissions: write-all
jobs:
workloads:
runs-on: ubuntu-22.04
steps:
# Configure AWS credentials
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v2
with:
role-to-assume: arn:aws:iam::593241322649:role/github-actions-madewithml
role-session-name: s3access
aws-region: us-west-2
# Set up dependencies
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.10.11'
cache: 'pip'
- run: python3 -m pip install anyscale==0.5.131 typer==0.9.0
# Run workloads
- name: Workloads
run: |
export ANYSCALE_HOST=${{ secrets.ANYSCALE_HOST }}
export ANYSCALE_CLI_TOKEN=${{ secrets.ANYSCALE_CLI_TOKEN }}
anyscale jobs submit deploy/jobs/workloads.yaml --wait
# Read results from S3
- name: Read results from S3
run: |
mkdir results
aws s3 cp s3://madewithml/${{ github.actor }}/results/ results/ --recursive
python .github/workflows/json_to_md.py results/training_results.json results/training_results.md
python .github/workflows/json_to_md.py results/evaluation_results.json results/evaluation_results.md
# Comment results to PR
- name: Comment training results on PR
uses: thollander/actions-comment-pull-request@v2
with:
filePath: results/training_results.md
- name: Comment evaluation results on PR
uses: thollander/actions-comment-pull-request@v2
with:
filePath: results/evaluation_results.md
================================================
FILE: .gitignore
================================================
# Data
logs/
stores/
mlflow/
results/
workspaces/
efs/
# VSCode
.vscode/
.idea
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Flask:
instance/
.webassets-cache
# Scrapy:
.scrapy
# Sphinx
docs/_build/
# PyBuilder
target/
# IPython
.ipynb_checkpoints
profile_default/
ipython_config.py
# pyenv
.python-version
# PEP 582
__pypackages__/
# Celery
celerybeat-schedule
celerybeat.pid
# Environment
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# mkdocs
site/
# Airflow
airflow/airflow.db
# MacOS
.DS_Store
# Clean up
.trash/
================================================
FILE: .pre-commit-config.yaml
================================================
# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-merge-conflict
- id: check-yaml
- id: check-added-large-files
args: ['--maxkb=1000']
exclude: "notebooks"
- id: check-yaml
exclude: "mkdocs.yml"
- repo: local
hooks:
- id: clean
name: clean
entry: make
args: ["clean"]
language: system
pass_filenames: false
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2023 Made With ML
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: Makefile
================================================
# Makefile
SHELL = /bin/bash
# Styling
.PHONY: style
style:
black .
flake8
python3 -m isort .
pyupgrade
# Cleaning
.PHONY: clean
clean: style
python notebooks/clear_cell_nums.py
find . -type f -name "*.DS_Store" -ls -delete
find . | grep -E "(__pycache__|\.pyc|\.pyo)" | xargs rm -rf
find . | grep -E ".pytest_cache" | xargs rm -rf
find . | grep -E ".ipynb_checkpoints" | xargs rm -rf
rm -rf .coverage*
================================================
FILE: README.md
================================================
<div align="center">
<h1><img width="30" src="https://madewithml.com/static/images/rounded_logo.png"> <a href="https://madewithml.com/">Made With ML</a></h1>
Design · Develop · Deploy · Iterate
<br>
Join 40K+ developers in learning how to responsibly deliver value with ML.
<br>
</div>
<br>
<div align="center">
<a target="_blank" href="https://madewithml.com/"><img src="https://img.shields.io/badge/Subscribe-40K-brightgreen"></a>
<a target="_blank" href="https://github.com/GokuMohandas/Made-With-ML"><img src="https://img.shields.io/github/stars/GokuMohandas/Made-With-ML.svg?style=social&label=Star"></a>
<a target="_blank" href="https://www.linkedin.com/in/goku"><img src="https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social"></a>
<a target="_blank" href="https://twitter.com/GokuMohandas"><img src="https://img.shields.io/twitter/follow/GokuMohandas.svg?label=Follow&style=social"></a>
<br>
🔥 Among the <a href="https://github.com/GokuMohandas/Made-With-ML" target="_blank">top ML repositories</a> on GitHub
</div>
<br>
<hr>
## Lessons
Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.
- Lessons: https://madewithml.com/
- Code: [GokuMohandas/Made-With-ML](https://github.com/GokuMohandas/Made-With-ML)
<a href="https://madewithml.com/#course">
<img src="https://madewithml.com/static/images/lessons.png" alt="lessons">
</a>
## Overview
In this course, we'll go from experimentation (design + development) to production (deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a *reliable* production system.
<blockquote>
<img width=20 src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/09/YouTube_full-color_icon_%282017%29.svg/640px-YouTube_full-color_icon_%282017%29.svg.png"> Be sure to watch the video below for a quick overview of what we'll be building.
</blockquote>
<div align="center">
<a href="https://youtu.be/AWgkt8H8yVo"><img src="https://img.youtube.com/vi/AWgkt8H8yVo/0.jpg" alt="Course overview video"></a>
</div>
<br>
- **💡 First principles**: before we jump straight into the code, we develop a first principles understanding for every machine learning concept.
- **💻 Best practices**: implement software engineering best practices as we develop and deploy our machine learning models.
- **📈 Scale**: easily scale ML workloads (data, train, tune, serve) in Python without having to learn completely new languages.
- **⚙️ MLOps**: connect MLOps components (tracking, testing, serving, orchestration, etc.) as we build an end-to-end machine learning system.
- **🚀 Dev to Prod**: learn how to quickly and reliably go from development to production without any changes to our code or infra management.
- **🐙 CI/CD**: learn how to create mature CI/CD workflows to continuously train and deploy better models in a modular way that integrates with any stack.
## Audience
Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person.
- **👩💻 All developers**: whether software/infra engineer or data scientist, ML is increasingly becoming a key part of the products that you'll be developing.
- **👩🎓 College graduates**: learn the practical skills required for industry and bridge gap between the university curriculum and what industry expects.
- **👩💼 Product/Leadership**: who want to develop a technical foundation so that they can build amazing (and reliable) products powered by machine learning.
## Set up
Be sure to go through the [course](https://madewithml/#course) for a much more detailed walkthrough of the content on this repository. We will have instructions for both local laptop and Anyscale clusters for the sections below, so be sure to toggle the ► dropdown based on what you're using (Anyscale instructions will be toggled on by default). If you do want to run this course with Anyscale, where we'll provide the **structure**, **compute (GPUs)** and **community** to learn everything in one day, join our next upcoming live cohort → [sign up here](https://4190urw86oh.typeform.com/madewithml)!
### Cluster
We'll start by setting up our cluster with the environment and compute configurations.
<details>
<summary>Local</summary><br>
Your personal laptop (single machine) will act as the cluster, where one CPU will be the head node and some of the remaining CPU will be the worker nodes. All of the code in this course will work in any personal laptop though it will be slower than executing the same workloads on a larger cluster.
</details>
<details open>
<summary>Anyscale</summary><br>
We can create an [Anyscale Workspace](https://docs.anyscale.com/develop/workspaces/get-started) using the [webpage UI](https://console.anyscale.com/o/madewithml/workspaces/add/blank).
```md
- Workspace name: `madewithml`
- Project: `madewithml`
- Cluster environment name: `madewithml-cluster-env`
# Toggle `Select from saved configurations`
- Compute config: `madewithml-cluster-compute-g5.4xlarge`
```
> Alternatively, we can use the [CLI](https://docs.anyscale.com/reference/anyscale-cli) to create the workspace via `anyscale workspace create ...`
</details>
<details>
<summary>Other (cloud platforms, K8s, on-prem)</summary><br>
If you don't want to do this course locally or via Anyscale, you have the following options:
- On [AWS and GCP](https://docs.ray.io/en/latest/cluster/vms/index.html#cloud-vm-index). Community-supported Azure and Aliyun integrations also exist.
- On [Kubernetes](https://docs.ray.io/en/latest/cluster/kubernetes/index.html#kuberay-index), via the officially supported KubeRay project.
- Deploy Ray manually [on-prem](https://docs.ray.io/en/latest/cluster/vms/user-guides/launching-clusters/on-premises.html#on-prem) or onto platforms [not listed here](https://docs.ray.io/en/latest/cluster/vms/user-guides/community/index.html#ref-cluster-setup).
</details>
### Git setup
Create a repository by following these instructions: [Create a new repository](https://github.com/new) → name it `Made-With-ML` → Toggle `Add a README file` (**very important** as this creates a `main` branch) → Click `Create repository` (scroll down)
Now we're ready to clone the repository that has all of our code:
```bash
git clone https://github.com/GokuMohandas/Made-With-ML.git .
```
### Credentials
```bash
touch .env
```
```bash
# Inside .env
GITHUB_USERNAME="CHANGE_THIS_TO_YOUR_USERNAME" # ← CHANGE THIS
```
```bash
source .env
```
### Virtual environment
<details>
<summary>Local</summary><br>
```bash
export PYTHONPATH=$PYTHONPATH:$PWD
python3 -m venv venv # recommend using Python 3.10
source venv/bin/activate # on Windows: venv\Scripts\activate
python3 -m pip install --upgrade pip setuptools wheel
python3 -m pip install -r requirements.txt
pre-commit install
pre-commit autoupdate
```
> Highly recommend using Python `3.10` and using [pyenv](https://github.com/pyenv/pyenv) (mac) or [pyenv-win](https://github.com/pyenv-win/pyenv-win) (windows).
</details>
<details open>
<summary>Anyscale</summary><br>
Our environment with the appropriate Python version and libraries is already all set for us through the cluster environment we used when setting up our Anyscale Workspace. So we just need to run these commands:
```bash
export PYTHONPATH=$PYTHONPATH:$PWD
pre-commit install
pre-commit autoupdate
```
</details>
## Notebook
Start by exploring the [jupyter notebook](notebooks/madewithml.ipynb) to interactively walkthrough the core machine learning workloads.
<div align="center">
<img src="https://madewithml.com/static/images/mlops/systems-design/workloads.png">
</div>
<details>
<summary>Local</summary><br>
```bash
# Start notebook
jupyter lab notebooks/madewithml.ipynb
```
</details>
<details open>
<summary>Anyscale</summary><br>
Click on the Jupyter icon <img width=15 src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/1200px-Jupyter_logo.svg.png"> at the top right corner of our Anyscale Workspace page and this will open up our JupyterLab instance in a new tab. Then navigate to the `notebooks` directory and open up the `madewithml.ipynb` notebook.
</details>
## Scripts
Now we'll execute the same workloads using the clean Python scripts following software engineering best practices (testing, documentation, logging, serving, versioning, etc.) The code we've implemented in our notebook will be refactored into the following scripts:
```bash
madewithml
├── config.py
├── data.py
├── evaluate.py
├── models.py
├── predict.py
├── serve.py
├── train.py
├── tune.py
└── utils.py
```
**Note**: Change the `--num-workers`, `--cpu-per-worker`, and `--gpu-per-worker` input argument values below based on your system's resources. For example, if you're on a local laptop, a reasonable configuration would be `--num-workers 6 --cpu-per-worker 1 --gpu-per-worker 0`.
### Training
```bash
export EXPERIMENT_NAME="llm"
export DATASET_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/dataset.csv"
export TRAIN_LOOP_CONFIG='{"dropout_p": 0.5, "lr": 1e-4, "lr_factor": 0.8, "lr_patience": 3}'
python madewithml/train.py \
--experiment-name "$EXPERIMENT_NAME" \
--dataset-loc "$DATASET_LOC" \
--train-loop-config "$TRAIN_LOOP_CONFIG" \
--num-workers 1 \
--cpu-per-worker 3 \
--gpu-per-worker 1 \
--num-epochs 10 \
--batch-size 256 \
--results-fp results/training_results.json
```
### Tuning
```bash
export EXPERIMENT_NAME="llm"
export DATASET_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/dataset.csv"
export TRAIN_LOOP_CONFIG='{"dropout_p": 0.5, "lr": 1e-4, "lr_factor": 0.8, "lr_patience": 3}'
export INITIAL_PARAMS="[{\"train_loop_config\": $TRAIN_LOOP_CONFIG}]"
python madewithml/tune.py \
--experiment-name "$EXPERIMENT_NAME" \
--dataset-loc "$DATASET_LOC" \
--initial-params "$INITIAL_PARAMS" \
--num-runs 2 \
--num-workers 1 \
--cpu-per-worker 3 \
--gpu-per-worker 1 \
--num-epochs 10 \
--batch-size 256 \
--results-fp results/tuning_results.json
```
### Experiment tracking
We'll use [MLflow](https://mlflow.org/) to track our experiments and store our models and the [MLflow Tracking UI](https://www.mlflow.org/docs/latest/tracking.html#tracking-ui) to view our experiments. We have been saving our experiments to a local directory but note that in an actual production setting, we would have a central location to store all of our experiments. It's easy/inexpensive to spin up your own MLflow server for all of your team members to track their experiments on or use a managed solution like [Weights & Biases](https://wandb.ai/site), [Comet](https://www.comet.ml/), etc.
```bash
export MODEL_REGISTRY=$(python -c "from madewithml import config; print(config.MODEL_REGISTRY)")
mlflow server -h 0.0.0.0 -p 8080 --backend-store-uri $MODEL_REGISTRY
```
<details>
<summary>Local</summary><br>
If you're running this notebook on your local laptop then head on over to <a href="http://localhost:8080/" target="_blank">http://localhost:8080/</a> to view your MLflow dashboard.
</details>
<details open>
<summary>Anyscale</summary><br>
If you're on <a href="https://docs.anyscale.com/develop/workspaces/get-started" target="_blank">Anyscale Workspaces</a>, then we need to first expose the port of the MLflow server. Run the following command on your Anyscale Workspace terminal to generate the public URL to your MLflow server.
```bash
APP_PORT=8080
echo https://$APP_PORT-port-$ANYSCALE_SESSION_DOMAIN
```
</details>
### Evaluation
```bash
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
export HOLDOUT_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/holdout.csv"
python madewithml/evaluate.py \
--run-id $RUN_ID \
--dataset-loc $HOLDOUT_LOC \
--results-fp results/evaluation_results.json
```
```json
{
"timestamp": "June 09, 2023 09:26:18 AM",
"run_id": "6149e3fec8d24f1492d4a4cabd5c06f6",
"overall": {
"precision": 0.9076136428670714,
"recall": 0.9057591623036649,
"f1": 0.9046792827719773,
"num_samples": 191.0
},
...
```
### Inference
```bash
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
python madewithml/predict.py predict \
--run-id $RUN_ID \
--title "Transfer learning with transformers" \
--description "Using transformers for transfer learning on text classification tasks."
```
```json
[{
"prediction": [
"natural-language-processing"
],
"probabilities": {
"computer-vision": 0.0009767753,
"mlops": 0.0008223939,
"natural-language-processing": 0.99762577,
"other": 0.000575123
}
}]
```
### Serving
<details>
<summary>Local</summary><br>
```bash
# Start
ray start --head
```
```bash
# Set up
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
python madewithml/serve.py --run_id $RUN_ID
```
Once the application is running, we can use it via cURL, Python, etc.:
```python
# via Python
import json
import requests
title = "Transfer learning with transformers"
description = "Using transformers for transfer learning on text classification tasks."
json_data = json.dumps({"title": title, "description": description})
requests.post("http://127.0.0.1:8000/predict", data=json_data).json()
```
```bash
ray stop # shutdown
```
</details>
<details open>
<summary>Anyscale</summary><br>
In Anyscale Workspaces, Ray is already running so we don't have to manually start/shutdown like we have to do locally.
```bash
# Set up
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
python madewithml/serve.py --run_id $RUN_ID
```
Once the application is running, we can use it via cURL, Python, etc.:
```python
# via Python
import json
import requests
title = "Transfer learning with transformers"
description = "Using transformers for transfer learning on text classification tasks."
json_data = json.dumps({"title": title, "description": description})
requests.post("http://127.0.0.1:8000/predict", data=json_data).json()
```
</details>
### Testing
```bash
# Code
python3 -m pytest tests/code --verbose --disable-warnings
# Data
export DATASET_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/dataset.csv"
pytest --dataset-loc=$DATASET_LOC tests/data --verbose --disable-warnings
# Model
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
pytest --run-id=$RUN_ID tests/model --verbose --disable-warnings
# Coverage
python3 -m pytest tests/code --cov madewithml --cov-report html --disable-warnings # html report
python3 -m pytest tests/code --cov madewithml --cov-report term --disable-warnings # terminal report
```
## Production
From this point onwards, in order to deploy our application into production, we'll need to either be on Anyscale or on a [cloud VM](https://docs.ray.io/en/latest/cluster/vms/index.html#cloud-vm-index) / [on-prem](https://docs.ray.io/en/latest/cluster/vms/user-guides/launching-clusters/on-premises.html#on-prem) cluster you manage yourself (w/ Ray). If not on Anyscale, the commands will be [slightly different](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/index.html) but the concepts will be the same.
> If you don't want to set up all of this yourself, we highly recommend joining our [upcoming live cohort](https://4190urw86oh.typeform.com/madewithml){:target="_blank"} where we'll provide an environment with all of this infrastructure already set up for you so that you just focused on the machine learning.
<div align="center">
<img src="https://madewithml.com/static/images/mlops/jobs_and_services/manual.png">
</div>
### Authentication
These credentials below are **automatically** set for us if we're using Anyscale Workspaces. We **do not** need to set these credentials explicitly on Workspaces but we do if we're running this locally or on a cluster outside of where our Anyscale Jobs and Services are configured to run.
``` bash
export ANYSCALE_HOST=https://console.anyscale.com
export ANYSCALE_CLI_TOKEN=$YOUR_CLI_TOKEN # retrieved from Anyscale credentials page
```
### Cluster environment
The cluster environment determines **where** our workloads will be executed (OS, dependencies, etc.) We've already created this [cluster environment](./deploy/cluster_env.yaml) for us but this is how we can create/update one ourselves.
```bash
export CLUSTER_ENV_NAME="madewithml-cluster-env"
anyscale cluster-env build deploy/cluster_env.yaml --name $CLUSTER_ENV_NAME
```
### Compute configuration
The compute configuration determines **what** resources our workloads will be executes on. We've already created this [compute configuration](./deploy/cluster_compute.yaml) for us but this is how we can create it ourselves.
```bash
export CLUSTER_COMPUTE_NAME="madewithml-cluster-compute-g5.4xlarge"
anyscale cluster-compute create deploy/cluster_compute.yaml --name $CLUSTER_COMPUTE_NAME
```
### Anyscale jobs
Now we're ready to execute our ML workloads. We've decided to combine them all together into one [job](./deploy/jobs/workloads.yaml) but we could have also created separate jobs for each workload (train, evaluate, etc.) We'll start by editing the `$GITHUB_USERNAME` slots inside our [`workloads.yaml`](./deploy/jobs/workloads.yaml) file:
```yaml
runtime_env:
working_dir: .
upload_path: s3://madewithml/$GITHUB_USERNAME/jobs # <--- CHANGE USERNAME (case-sensitive)
env_vars:
GITHUB_USERNAME: $GITHUB_USERNAME # <--- CHANGE USERNAME (case-sensitive)
```
The `runtime_env` here specifies that we should upload our current `working_dir` to an S3 bucket so that all of our workers when we execute an Anyscale Job have access to the code to use. The `GITHUB_USERNAME` is used later to save results from our workloads to S3 so that we can retrieve them later (ex. for serving).
Now we're ready to submit our job to execute our ML workloads:
```bash
anyscale job submit deploy/jobs/workloads.yaml
```
### Anyscale Services
And after our ML workloads have been executed, we're ready to launch our serve our model to production. Similar to our Anyscale Jobs configs, be sure to change the `$GITHUB_USERNAME` in [`serve_model.yaml`](./deploy/services/serve_model.yaml).
```yaml
ray_serve_config:
import_path: deploy.services.serve_model:entrypoint
runtime_env:
working_dir: .
upload_path: s3://madewithml/$GITHUB_USERNAME/services # <--- CHANGE USERNAME (case-sensitive)
env_vars:
GITHUB_USERNAME: $GITHUB_USERNAME # <--- CHANGE USERNAME (case-sensitive)
```
Now we're ready to launch our service:
```bash
# Rollout service
anyscale service rollout -f deploy/services/serve_model.yaml
# Query
curl -X POST -H "Content-Type: application/json" -H "Authorization: Bearer $SECRET_TOKEN" -d '{
"title": "Transfer learning with transformers",
"description": "Using transformers for transfer learning on text classification tasks."
}' $SERVICE_ENDPOINT/predict/
# Rollback (to previous version of the Service)
anyscale service rollback -f $SERVICE_CONFIG --name $SERVICE_NAME
# Terminate
anyscale service terminate --name $SERVICE_NAME
```
### CI/CD
We're not going to manually deploy our application every time we make a change. Instead, we'll automate this process using GitHub Actions!
<div align="center">
<img src="https://madewithml.com/static/images/mlops/cicd/cicd.png">
</div>
1. Create a new github branch to save our changes to and execute CI/CD workloads:
```bash
git remote set-url origin https://github.com/$GITHUB_USERNAME/Made-With-ML.git # <-- CHANGE THIS to your username
git checkout -b dev
```
2. We'll start by adding the necessary credentials to the [`/settings/secrets/actions`](https://github.com/GokuMohandas/Made-With-ML/settings/secrets/actions) page of our GitHub repository.
``` bash
export ANYSCALE_HOST=https://console.anyscale.com
export ANYSCALE_CLI_TOKEN=$YOUR_CLI_TOKEN # retrieved from https://console.anyscale.com/o/madewithml/credentials
```
3. Now we can make changes to our code (not on `main` branch) and push them to GitHub. But in order to push our code to GitHub, we'll need to first authenticate with our credentials before pushing to our repository:
```bash
git config --global user.name $GITHUB_USERNAME # <-- CHANGE THIS to your username
git config --global user.email you@example.com # <-- CHANGE THIS to your email
git add .
git commit -m "" # <-- CHANGE THIS to your message
git push origin dev
```
Now you will be prompted to enter your username and password (personal access token). Follow these steps to get personal access token: [New GitHub personal access token](https://github.com/settings/tokens/new) → Add a name → Toggle `repo` and `workflow` → Click `Generate token` (scroll down) → Copy the token and paste it when prompted for your password.
4. Now we can start a PR from this branch to our `main` branch and this will trigger the [workloads workflow](/.github/workflows/workloads.yaml). If the workflow (Anyscale Jobs) succeeds, this will produce comments with the training and evaluation results directly on the PR.
<div align="center">
<img src="https://madewithml.com/static/images/mlops/cicd/comments.png">
</div>
5. If we like the results, we can merge the PR into the `main` branch. This will trigger the [serve workflow](/.github/workflows/serve.yaml) which will rollout our new service to production!
### Continual learning
With our CI/CD workflow in place to deploy our application, we can now focus on continually improving our model. It becomes really easy to extend on this foundation to connect to scheduled runs (cron), [data pipelines](https://madewithml.com/courses/mlops/data-engineering/), drift detected through [monitoring](https://madewithml.com/courses/mlops/monitoring/), [online evaluation](https://madewithml.com/courses/mlops/evaluation/#online-evaluation), etc. And we can easily add additional context such as comparing any experiment with what's currently in production (directly in the PR even), etc.
<div align="center">
<img src="https://madewithml.com/static/images/mlops/cicd/continual.png">
</div>
## FAQ
### Jupyter notebook kernels
Issues with configuring the notebooks with jupyter? By default, jupyter will use the kernel with our virtual environment but we can also manually add it to jupyter:
```bash
python3 -m ipykernel install --user --name=venv
```
Now we can open up a notebook → Kernel (top menu bar) → Change Kernel → `venv`. To ever delete this kernel, we can do the following:
```bash
jupyter kernelspec list
jupyter kernelspec uninstall venv
```
================================================
FILE: datasets/dataset.csv
================================================
id,created_on,title,description,tag
6,2020-02-20 06:43:18,Comparison between YOLO and RCNN on real world videos,Bringing theory to experiment is cool. We can easily train models in colab and find the results in minutes.,computer-vision
7,2020-02-20 06:47:21,"Show, Infer & Tell: Contextual Inference for Creative Captioning","The beauty of the work lies in the way it architects the fundamental idea that humans look at the overall image and then individual pieces of it.
",computer-vision
9,2020-02-24 16:24:45,Awesome Graph Classification,"A collection of important graph embedding, classification and representation learning papers with implementations.",other
15,2020-02-28 23:55:26,Awesome Monte Carlo Tree Search,A curated list of Monte Carlo tree search papers with implementations. ,other
25,2020-03-07 23:04:31,AttentionWalk,"A PyTorch Implementation of ""Watch Your Step: Learning Node Embeddings via Graph Attention"" (NeurIPS 2018). ",other
27,2020-03-07 23:18:15,APPNP and PPNP,"A PyTorch implementation of ""Predict then Propagate: Graph Neural Networks meet Personalized PageRank"" (ICLR 2019). ",other
28,2020-03-07 23:23:46,Attributed Social Network Embedding,"A sparsity aware and memory efficient implementation of ""Attributed Social Network Embedding"" (TKDE 2018). ",other
29,2020-03-07 23:45:38,Signed Graph Convolutional Network,"A PyTorch implementation of ""Signed Graph Convolutional Network"" (ICDM 2018). ",other
45,2020-03-08 00:39:08,SimGNN,"A PyTorch implementation of ""SimGNN: A Neural Network Approach to Fast Graph Similarity Computation"" (WSDM 2019). ",other
61,2020-03-16 17:35:22,Using JAX to Improve Separable Image Filters,Optimizing the filters to improve the filtered images for computer vision tasks.,computer-vision
65,2020-03-19 18:42:05,Coloring Greyscale Images,Coloring black and white images with neural networks.,computer-vision
67,2020-03-19 19:04:43,Fruit Detection using Convolution Neural Networks in TensorFlow,"Trained a Convolutional Neural Network Model to predict fruits of over 100+ Classes (types) with a training accuracy of over 95%, and testing accuracy of over 9",computer-vision
73,2020-03-19 23:45:14,Face Verification,Implementation of Siamese Neural network model used for face verification. The dataset used for this task is IMDB-WIKI-face images Dataset.,computer-vision
77,2020-03-20 03:23:27,Sign Language Interpreter using Deep Learning,"A sign language interpreter using live video feed from the camera. The project was completed in 24 hours as part of HackUNT-19, the University of North Texas's ",computer-vision
78,2020-03-20 03:32:09,The Illustrated Self-Supervised Learning,A visual introduction to self-supervised learning methods in Computer Vision,computer-vision
81,2020-03-20 06:07:56,GradCAM for the BreaKHis Dataset,An NBDev package for fine-tuning ResNets to visualize gradient-weighted class activation for the BreaKHis dataset.,computer-vision
85,2020-03-20 17:35:59,Message Passing GNNs C++,C++ implementation using Eigen for the forward pass of Graph Convolutional Neural Networks.,other
89,2020-03-20 18:17:31,Rethinking Batch Normalization in Transformers,"We found that NLP batch statistics exhibit large variance throughout training, which leads to poor BN performance.",natural-language-processing
91,2020-03-20 18:30:04,Pytest Board,Continuous pytest runner with awesome visualization.,mlops
92,2020-03-20 18:43:50,Image Spam Buster - Kreate Hackathon,"""Spam Buster"" for user generated IMAGE content.",computer-vision
98,2020-03-20 19:16:43,Bachelorette Predictor,Predict the Bachelorette winners from profile images.,computer-vision
99,2020-03-20 21:32:14,Gender Change of People's Face using CycleGAN,CycleGAN architecture in Keras and train the model with CelebA faces dataset to perform gender change on people's faces.,computer-vision
101,2020-03-21 04:19:04,ELECTRA: Pre-training Text Encoders as Discriminators,PyTorch implementation of the electra model from the paper: ELECTRA - Pre-training Text Encoders as Discriminators Rather Than Generators,natural-language-processing
108,2020-03-21 23:17:38,Tuned ALBERT (ensemble model),Top 6 in Squad 2.0,natural-language-processing
109,2020-03-21 23:25:33,iyasai: Book Recommendation System,Recommender system for books and stories that could help you and your loved ones lift up your mood whenever you are facing stress or unpleasant situations.,natural-language-processing
112,2020-03-21 23:58:46,Learning to See before Learning to Act: Visual Pre-training,We find that pre-training on vision tasks significantly improves generalization and sample efficiency for learning to manipulate objects.,computer-vision
115,2020-03-22 01:26:14,SOLT: Data Augmentation for Deep Learning,"Data augmentation library for Deep Learning, which supports images, segmentation masks, labels and key points.",computer-vision
116,2020-03-22 01:37:27,PCDet: 3D Point Cloud Detection,PCDet Toolbox in PyTorch for 3D Object Detection from Point Cloud,computer-vision
117,2020-03-22 01:47:09,SiamFC++: Towards Robust and Accurate Visual Tracking,"Implementation of a series of basic algorithms which is useful for video understanding, including Single Object Tracking (SOT), Video Object Segmentation (VOS).",computer-vision
118,2020-03-22 21:46:52,Sinext,Sign language to text with OpenCV and MNIST sign-language dataset,computer-vision
120,2020-03-24 04:38:08,Gliding Vertex on Horizontal Bounding Box for Object Detection,Gliding vertex on the horizontal bounding box for multi-oriented object detection.,computer-vision
121,2020-03-24 04:56:38,Deep Reinforcement Learning in TensorFlow2,deep-rl-tf2 is a repository that implements a variety of polular Deep-RL algorithms using TF2. The key to this repo is an easy to understand code. ,other
122,2020-03-24 17:51:35,Custom Classifier on Top of Bert-like Language Model,Take pre-trained language model and build custom classifier on top of it.,natural-language-processing
123,2020-03-24 18:20:55,Using Different Decoding Methods for LM with Transformers,A look at different decoding methods for generate subsequent tokens in language modeling.,natural-language-processing
124,2020-03-24 21:12:12,Unsupervised Toolbox,"Unsupervised learning Tool box : A micro framework for State of the Art Methods and models for unsupervised learning for NLU / NLG
",natural-language-processing
128,2020-03-25 15:21:34,Multimodal Brain Tumor Segmentation,Segmentation of gliomas in pre-operative MRI scans. Use the provided clinically-acquired training data to produce segmentation labels.,computer-vision
133,2020-03-25 20:21:26,A Survey of Long-Term Context in Transformers,Over the past two years the NLP community has developed a veritable zoo of methods to combat expensive multi-head self-attention.,natural-language-processing
137,2020-03-27 14:39:53,Debugging Neural Networks with PyTorch and W&B,A closer look at debugging common issues when training neural networks.,mlops
138,2020-03-27 14:50:02,BachGAN: High-Res Image Synthesis from Salient Object Layout,We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout. ,computer-vision
140,2020-03-28 07:49:03,Visual Paper Summary: ALBERT(A Lite BERT),An illustrated summary of ALBERT paper and how it improves BERT and makes it resource efficient,natural-language-processing
145,2020-03-30 04:14:44,Controllable Person Image Synthesis with Attribute-Decomposed GAN,"A novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes.",computer-vision
147,2020-03-30 05:39:57,Back Translation for Text Augmentation with Google Sheets,Learn how to augment existing labeled text data for free using Google Sheets.,natural-language-processing
148,2020-03-30 14:13:46,An Illustrated Guide to Graph Neural Networks,A breakdown of the inner workings of GNNs.,other
150,2020-04-01 08:26:46,The Illustrated FixMatch for Semi-Supervised Learning,Learn how to leverage unlabeled data using FixMatch for semi-supervised learning,computer-vision
152,2020-04-01 15:38:58,A Two-Step Graph Convolutional Decoder for Molecule Generation,A simple auto-encoder framework for molecule generation.,other
157,2020-04-03 01:56:32,TransMoMo: Invariance-Driven Unsupervised Motion Retargeting,A lightweight video motion retargeting approach that is capable of transferring motion of a person in a source video realistically to another video of a target ,computer-vision
158,2020-04-03 04:41:07,Tracking Objects as Points,Simultaneous object detection and tracking using center points.,computer-vision
159,2020-04-03 14:57:11,Drifter-ML,A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.,mlops
162,2020-04-03 20:17:50,Natural Language Processing News,Get the highlights from Natural Language Processing & Machine Learning research & industry straight to your inbox every month.,natural-language-processing
163,2020-04-03 20:21:13,NLP Newsletter,"Democratizing Artificial Intelligence Research, Education, and Technologies.",natural-language-processing
168,2020-04-04 17:54:28,Self-Supervised Scene De-occlusion,"We investigate the problem of scene de-occlusion, which aims to recover the underlying occlusion ordering and complete the invisible parts of occluded objects.",computer-vision
173,2020-04-05 03:00:05,Design Patterns for Production NLP Systems,Designs and tips for designing NLP production systems.,natural-language-processing
181,2020-04-05 14:56:34,Talking-Heads Attention,"A variation on multi-head attention which includes linear projections across the attention-heads dimension, immediately before and after the softmax operation.",natural-language-processing
183,2020-04-05 17:50:10,What does a CNN see?,First super clean notebook showcasing @TensorFlow 2.0. An example of end-to-end DL with interpretability.,computer-vision
219,2020-04-06 14:10:22,Natural Language Processing: Pretraining - d2l,"An interactive deep learning book with code, math, and discussions, based on the NumPy interface.",natural-language-processing
224,2020-04-06 16:48:44,Understanding Convolutional Neural Networks for NLP,More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results.,natural-language-processing
234,2020-04-06 17:42:52,An Overview of Semantic Image Segmentation,Image segmentation is a computer vision task in which we label specific regions of an image according to what's being shown.,computer-vision
237,2020-04-06 18:02:48,Common Architectures in Convolutional Neural Networks,"In this post, I'll discuss commonly used architectures for convolutional networks. ",computer-vision
238,2020-04-06 18:37:33,Googletrans,Googletrans: Free and Unlimited Google translate API for Python. Translates totally free of charge.,natural-language-processing
239,2020-04-06 18:39:48,Prophet: Forecasting At Scale,Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.,other
250,2020-04-06 19:24:06,Doccano,Open source text annotation tool for machine learning practitioner. ,natural-language-processing
251,2020-04-06 19:28:58,BRAT: Rapid Annotation Tool,BRAT (brat rapid annotation tool) is based on the stav visualiser which was originally made in order to visualise BioNLP'11 Shared Task data.,natural-language-processing
252,2020-04-06 20:23:46,Word Embeddings,This tutorial introduces word embeddings. It contains complete code to train word embeddings from scratch on a small dataset.,natural-language-processing
253,2020-04-06 20:26:27,On Word Embeddings,This post presents the most well-known models for learning word embeddings based on language modeling.,natural-language-processing
254,2020-04-06 20:28:43,NLP for Developers: Word Embeddings | Rasa,"In this video, Rasa Developer Advocate Rachael will talk about what word embeddings are, how they work, when they're used and some common errors. ",natural-language-processing
255,2020-04-06 20:30:27,NLP for Developers: Transformers | Rasa,"In this video, Rasa Developer Advocate Rachael will talk about what transformers are, how they work, when they're used and some common errors. ",natural-language-processing
256,2020-04-06 20:42:05,A Visual Guide to Using BERT for the First Time,Tutorial for how to use a variant of BERT to classify sentences.,natural-language-processing
257,2020-04-06 20:45:45,The Illustrated GPT-2 (Visualizing Transformer Language Models),Visuals explaining the inner-workings of transformers.,natural-language-processing
259,2020-04-06 20:51:58,The Illustrated Word2vec,"In this post, we’ll go over the concept of embedding, and the mechanics of generating embeddings with word2vec. ",natural-language-processing
260,2020-04-06 20:55:32,"The Illustrated BERT, ELMo, and co.",How NLP cracked transfer learning.,natural-language-processing
261,2020-04-06 21:00:34,The Illustrated Transformer,"In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained.",natural-language-processing
262,2020-04-06 21:11:40,Visualizing A Neural Machine Translation Model,Mechanics of seq2seq models with attention.,natural-language-processing
269,2020-04-06 22:46:54,Attention Mechanism,"Main concepts behind Attention, including an implementation of a sequence-to-sequence Attention model, followed by the application of Attention in Transformers.",natural-language-processing
270,2020-04-06 22:50:30,Attention? Attention!,"In this post, we are gonna look into how attention was invented, and various attention mechanisms and models, such as transformer and SNAIL.",natural-language-processing
271,2020-04-06 22:58:47,The Annotated Transformer,In this post I present an “annotated” version of the paper in the form of a line-by-line implementation. ,natural-language-processing
272,2020-04-06 23:38:26,The Annotated GPT-2,GPT-2 explained with visualization and PyTorch code.,natural-language-processing
273,2020-04-06 23:41:52,Transformers - Hugging Face,🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch. ,natural-language-processing
277,2020-04-07 00:30:33,Curriculum for Reinforcement Learning,"Curriculum learning applied to reinforcement learning, with a few exceptions of supervised learning.",other
278,2020-04-07 00:34:46,Self-Supervised Representation Learning,What if we can get labels for free for unlabelled data and train unsupervised dataset in a supervised manner? ,computer-vision
279,2020-04-07 00:36:55,Evolution Strategies,Evolutionary algorithms refer to a division of population-based optimization algorithms inspired by natural selection. ,other
280,2020-04-07 00:38:25,Meta Reinforcement Learning,Explore cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently.,other
281,2020-04-07 00:40:59,Generalized Language Models,Trend in large unsupervised pre-trained language models which have achieved amazing SOTA results on a variety of language tasks.,natural-language-processing
284,2020-04-07 00:57:12,Policy Gradient Algorithms,"In this post, we are going to look deep into policy gradient, why it works, and many new policy gradient algorithms proposed in recent years.",other
286,2020-04-07 03:49:15,Object Detection for Dummies,"We will go through several basic concepts, algorithms, and popular deep learning models for image processing and object detection.",computer-vision
287,2020-04-07 03:59:53,Learning Word Embedding,This post introduces several models for learning word embedding and how their loss functions are designed for the purpose.,natural-language-processing
290,2020-04-07 13:38:36,GANSpace: Discovering Interpretable GAN Controls,This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis.,computer-vision
291,2020-04-07 14:07:59,Kornia: Differentiable Computer Vision Library for PyTorch,Set of routines and differentiable modules to solve generic computer vision problems. ,computer-vision
294,2020-04-07 15:36:13,PyTorch Geometric ,Geometric deep learning extension library for PyTorch.,other
295,2020-04-07 15:40:00,DGL: Deep Graph Library,"Python package built to ease deep learning on graph, on top of existing DL frameworks. ",other
306,2020-04-07 20:07:28,BERT Research - Key Concepts & Sources,Video series on BERT's key concepts and sources.,natural-language-processing
307,2020-04-07 20:11:29,GLUE Explained: Understanding BERT Through Benchmarks,In this post we take a look at an important NLP benchmark used to evaluate BERT and other transfer learning models!,natural-language-processing
308,2020-04-07 23:22:18,TinyBERT,TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding.,natural-language-processing
313,2020-04-08 00:02:27,NVIDIA Neural Modules: NeMo,A toolkit for conversational AI.,natural-language-processing
315,2020-04-08 00:10:21,VoTT: Visual Object Tagging Tool,An electron app for building end to end Object Detection Models from Images and Videos.,computer-vision
316,2020-04-08 00:12:26,Clinical BERT,Repository for Publicly Available Clinical BERT Embeddings,natural-language-processing
318,2020-04-08 00:16:55,Computer Vision Annotation Tool (CVAT),"Free, online, interactive video and image annotation tool for computer vision.",computer-vision
319,2020-04-08 00:19:04,LabelImg,🖍️ A graphical image annotation tool and label object bounding boxes in images.,computer-vision
327,2020-04-08 14:16:28,How to Steal Modern NLP Systems with Gibberish?,"It’s possible to steal BERT-based models without any real training data, even using gibberish word sequences.",natural-language-processing
334,2020-04-08 15:04:28,BioWordVec & BioSentVec,Pre-trained embeddings for biomedical words and sentences,natural-language-processing
335,2020-04-08 15:07:44,BioBERT: a pre-trained biomedical language representation model ,"Code for fine-tuning BioBERT for biomedical text mining tasks such as biomedical NER, relation extraction, QA, etc.",natural-language-processing
341,2020-04-08 15:42:56,How to Unit Test Machine Learning Code,Wouldn’t suck to have to throw away perfectly good ideas because our implementations were buggy?,mlops
343,2020-04-08 15:52:19,Machine Learning Systems Design,Designing a machine learning system.,mlops
345,2020-04-08 16:14:23,HMTL: Hierarchical Multi-Task Learning,🌊 A State-of-the-Art neural network model for several NLP tasks based on PyTorch and AllenNLP,natural-language-processing
347,2020-04-08 16:26:05,The State of Transfer Learning in NLP,This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. It highlights key insights and takeaways and provides updates based on recent work.,natural-language-processing
349,2020-04-08 16:35:52,The Dark Secrets of BERT,How much of the linguistically interpretable self-attention patterns that are presumed to be its strength are actually used to solve downstream tasks?,natural-language-processing
364,2020-04-08 17:53:15,Named Entity Recognition Tagging,"In this post, we go through an example from Natural Language Processing, in which we learn how to load text data and perform NER tagging for each token.",natural-language-processing
372,2020-04-08 18:22:46,An introduction to Q-Learning: Reinforcement Learning,Q-Learning algorithm along with an implementation in Python using Numpy.,other
378,2020-04-08 19:37:57,Ray,Ray is a fast and simple framework for building and running distributed applications.,other
380,2020-04-08 21:05:06,Graph Nets,"PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.",other
388,2020-04-08 21:36:39,ConvNet Playground,An interactive visualization for exploring Convolutional Neural Networks applied to the task of semantic image search.,computer-vision
392,2020-04-08 21:53:06,Embedding Projector,"Visualization of high dimensional data, namely embeddings.",natural-language-processing
395,2020-04-08 22:12:24,Word2Viz: Explore Word Analogies,Interactive visualization of word analogies in GloVe.,natural-language-processing
397,2020-04-08 22:17:06,Image-to-Image Translation with Conditional Adversarial Networks,Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets,computer-vision
401,2020-04-08 22:29:09,"Quick, Draw",Can a neural network learn to recognize doodling?,computer-vision
403,2020-04-08 22:44:04,A 2019 Guide to Speech Synthesis with Deep Learning,A look at recent deep learning based speech synthesis research and techniques.,natural-language-processing
408,2020-04-08 23:03:13,FlashTorch,Visualization toolkit for neural networks in PyTorch,computer-vision
411,2020-04-08 23:11:09,W&B: Weights and Biases,Track model training at scale.,mlops
419,2020-04-09 00:41:03,Text Feature Selection for Causal Inference,"Identifying the linguistic features that cause people to act a certain way after reading a text, regardless of confounding variables, is something people do.",natural-language-processing
423,2020-04-09 00:57:49,3D Ken Burns Effect from a Single Image,Implementation of 3D Ken Burns Effect from a Single Image using PyTorch.,computer-vision
424,2020-04-09 01:02:59,Sparse Sinkhorn Attention,A new efficient and sparse method for learning to attend based on differentiable sorting of internal representations.,natural-language-processing
425,2020-04-09 01:41:48,Backtester,A backtesting framework for timeseries data.,other
427,2020-04-09 18:57:01,An Overview of Early Vision in InceptionV1,"A guided tour of the first five layers of InceptionV1,
taxonomized into “neuron groups.”",computer-vision
428,2020-04-10 04:57:53,AiLight: Automatic Highlighting Using BERT,"Automatically highlight pdfs using BERT embeddings and clustering.
https://anishthite.github.io/ailight",natural-language-processing
430,2020-04-10 15:28:43,Controlling Text Generation with Plug and Play Language Models,"This article discusses an alternative approach to controlled text generation, titled the Plug and Play Language Model (PPLM).",natural-language-processing
431,2020-04-10 15:35:00,Genomic ULMFiT,ULMFiT for Genomic Sequence Data,natural-language-processing
432,2020-04-10 15:39:29,Self-Supervised Learning and Computer Vision,"So, what do you do if there are no pre-trained models in your domain? ",computer-vision
434,2020-04-10 15:51:52,scispaCy,A full spaCy pipeline and models for scientific/biomedical documents.,natural-language-processing
439,2020-04-10 17:33:38,Universal Adversarial Triggers for Attacking and Analyzing NLP,We create short phrases that cause a specific model prediction when concatenated to 𝘢𝘯𝘺 input from a dataset. ,natural-language-processing
440,2020-04-10 17:39:19,lazynlp,Library to scrape and clean web pages to create massive datasets.,natural-language-processing
443,2020-04-10 17:51:39,AllenNLP Interpret,A Framework for Explaining Predictions of NLP Models,natural-language-processing
445,2020-04-10 18:00:50,Natural Language Processing With spaCy in Python,A comprehensive guide to NLP with spaCy.,natural-language-processing
446,2020-04-10 18:45:15,Tips for Successfully Training Transformers on Small Datasets,It turns out that you can easily train transformers on small datasets when you use tricks (and have the patience to train a very long time).,natural-language-processing
448,2020-04-10 19:14:59,🦄 How to build a SOTA Conversational AI with Transfer Learning,Train a dialog agent leveraging transfer Learning from an OpenAI GPT and GPT-2 Transformer language model.,natural-language-processing
452,2020-04-10 20:18:20,CS224n: Natural Language Processing with Deep Learning,"In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP.",natural-language-processing
453,2020-04-10 20:23:21,CS231n: Convolutional Neural Networks for Visual Recognition,"Deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.",computer-vision
455,2020-04-10 20:31:09,Illustrated: Self-Attention,Step-by-step guide to self-attention with illustrations and code.,natural-language-processing
459,2020-04-10 21:05:32,Beyond the Pixel Plane: Sensing and Learning in 3d,Recent deep learning techniques that enable 3D object classification and semantic segmentation.,computer-vision
462,2020-04-11 16:52:35,A Visual Guide to Self-Labelling Images,A self-supervised method to generate labels via simultaneous clustering and representation learning,computer-vision
465,2020-04-13 02:18:51,3D Photography using Context-aware Layered Depth Inpainting,A multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. ,computer-vision
466,2020-04-13 18:48:40,Tokenizers: How Machines Read,A survey of different tokenization strategies in NLP.,natural-language-processing
467,2020-04-13 19:43:35,Practical Text Classification With Python and Keras,You will get a grasp of current advancements of (deep) neural networks and how they can be applied to text.,natural-language-processing
468,2020-04-13 19:45:46,Text Classification With Torchtext,This example shows how to train a supervised learning algorithm for classification using one of these TextClassification datasets.,natural-language-processing
469,2020-04-13 21:17:44,Understanding Text With Bert,Building a machine reading comprehension system using the latest advances in deep learning for NLP.,natural-language-processing
470,2020-04-13 21:38:20,Transfer Learning with T5: the Text-To-Text Transfer Transformer,"In the paper, we demonstrate how to achieve state-of-the-art results on multiple NLP tasks using a text-to-text transformer pre-trained on a large text corpus.",natural-language-processing
471,2020-04-13 21:48:48,Building a COVID-19 Project Recommendation System,"How to create a GitHub open source repo recommendation system web app with MLflow, Sagemaker, and Booklet.ai.",natural-language-processing
473,2020-04-13 22:33:21,Neural Machine Translation With Attention,This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation. ,natural-language-processing
474,2020-04-13 22:48:49,PyTorch Tutorial for Deep Learning Researchers,This repository provides tutorial code for deep learning researchers to learn PyTorch. ,computer-vision
476,2020-04-14 00:40:10,Show and Tell: A Neural Image Caption Generator,A TensorFlow implementation of the image-to-text model.,computer-vision
477,2020-04-14 01:46:32,SimpleGAN,A Tensorflow-based framework to ease the training of generative models,computer-vision
478,2020-04-14 02:41:43,Semantic Segmentation on MIT ADE20K dataset in PyTorch,Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset.,computer-vision
480,2020-04-14 03:46:09,ViLBERT-MT: Multi-Task Vision & Language Representation Learning,A single ViLBERT Multi-Task model can perform 8 different vision and language tasks learnt from 12 datasets!,computer-vision
481,2020-04-14 03:50:18,Training an Image Classifier in PyTorch,"Torchvision, that has data loaders for common datasets such as Imagenet, CIFAR10, MNIST, etc. and data transformers for images, vizualization and data loaders.",computer-vision
482,2020-04-14 17:28:37,A Visual Exploration of DeepCluster,DeepCluster is a self-supervised method to combine clustering and representation learning,computer-vision
486,2020-04-14 20:12:43,A 2019 guide to Human Pose Estimation with Deep Learning,The basics of Human Pose Estimation (2D) and review the literature on this topic.,computer-vision
489,2020-04-14 22:22:40,"Deep Learning Based Super Resolution, Without Using a GAN","Techniques and training a deep learning model for image improvement, image restoration, inpainting and super resolution.",computer-vision
490,2020-04-14 22:35:21,U-Net Deep Learning Colorization of Greyscale Images,This article describes experiments training a neural network to generate 3 channel colour images from single channel greyscale images using deep learning.,computer-vision
491,2020-04-14 22:38:54,Deep Learning for Image Super-resolution: A Survey,This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches.,computer-vision
492,2020-04-14 22:41:52,Second-order Attention Network for Single Image Super-resolution,We propose a second-order attention network (SAN) for more powerful feature expression and feature correlation learning.,computer-vision
493,2020-04-14 22:52:49,DeepSORT: Deep Learning to Track Custom Objects in a Video,A look at deep learning based approached for object tracking.,computer-vision
494,2020-04-14 22:59:56,Fast Online Object Tracking and Segmentation: A Unifying Approach,We illustrate how to perform both realtime object tracking and semi-supervised video object segmentation using a fully-convolutional Siamese approach.,computer-vision
495,2020-04-14 23:10:48,Neural Style Transfer,This tutorial uses deep learning to compose one image in the style of another image (ever wish you could paint like Picasso or Van Gogh?).,computer-vision
499,2020-04-14 23:34:32,Deep Learning for Videos: A 2018 Guide to Action Recognition,"In this post, I summarize the literature on action recognition from videos. ",computer-vision
501,2020-04-15 15:20:56,Shakespeare Meets Google's Flax,Application of RNNs in Flax: Character-Level Language Model.,natural-language-processing
505,2020-04-15 15:59:30,"Anomaly detection with Keras, TensorFlow, and Deep Learning",Perform anomaly detection in your own image datasets using deep learning.,computer-vision
507,2020-04-15 16:12:41,Almost Everything You Need to Know About Time Series,"Understand moving average, exponential smoothing, stationarity, autocorrelation, SARIMA, and more.",other
508,2020-04-15 16:29:08,STEFANN: Scene Text Editor using Font Adaptive Neural Network,A generalized method for realistic modification of textual content present in a scene image. ⭐️ Accepted in CVPR 2020.,computer-vision
509,2020-04-15 16:34:04,Time Series Prediction with LSTM Using PyTorch,Time series applied to forecasting on the Airplane Passengers Dataset.,other
513,2020-04-15 17:05:36,lda2vec: Tools for interpreting natural language,The lda2vec model tries to mix the best parts of word2vec and LDA into a single framework.,natural-language-processing
516,2020-04-15 17:21:53,Deep Learning for Object Detection: A Comprehensive Review,"A closer look at Tensorflow’s object detection models: Faster R-CNN, R-FCN, and SSD.",computer-vision
517,2020-04-15 17:31:22,An Intuitive Guide to Deep Network Architectures,"Intuition behind base network architectures like MobileNets, Inception, and ResNet.",computer-vision
529,2020-04-15 19:39:24,Real-Time Voice Cloning,Clone a voice in 5 seconds to generate arbitrary speech in real-time. Code for Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech.,natural-language-processing
549,2020-04-16 03:48:35,15 Best Tools for Tracking Machine Learning Experiments,A feature comparison of all the open-source and commercial options for experiment tracking.,mlops
550,2020-04-16 08:14:50,Cycle GAN in TensorFlow 2.0 with Custom Loops,"Implementation of ""Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks"" by Jun-Yan Zhu et al. ",computer-vision
552,2020-04-16 10:13:12,Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset,The largest dataset of in-the-wild stereo image pairs (50K) crowd-sourced from the Holopix lightfield image-sharing social network.,computer-vision
558,2020-04-16 15:49:29,PyTorch Notebooks,🔥A collection of PyTorch notebooks for learning and practicing deep learning,natural-language-processing
564,2020-04-17 13:16:09,Optimize your ML models,Learn to use optimize your custom image classification models (built-in tf.keras) using TensorFlow Lite and gain 10x reduction in model's size. ,computer-vision
566,2020-04-17 21:57:35,Machine learning deserves its own flavor of Continuous Delivery,"When traveling in the data science world, I'm homesick for a smooth continuous delivery flow. My thoughts on approachable CD4ML.",mlops
574,2020-04-20 00:23:44,The Abstraction and Reasoning Corpus (ARC),"Can a computer learn complex, abstract tasks from just a few examples? ARC can be used to measure a human-like form of general fluid intelligence.",natural-language-processing
580,2020-04-20 00:57:03,GitHub Actions & Machine Learning Workflows with Hamel Husain," In this talk, Hamel will provide a brief tutorial on GitHub Actions, and will show you how you can use this new tool to automate your ML workflows.",mlops
581,2020-04-20 01:01:38,How To Create Semantic Search For Arbitrary Objects,An end-to-end example of how to build a system that can search objects semantically. By Hamel Husain & Ho-Hsiang Wu,natural-language-processing
598,2020-04-22 16:33:59,The Future of (Transfer Learning in) Natural Language Processing,"Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions.",natural-language-processing
599,2020-04-22 16:43:13,MONAI,AI Toolkit for Healthcare Imaging.,computer-vision
601,2020-04-22 17:41:06,How I Used Deep Learning To Train A Chatbot To Talk Like Me,Facebook chatbot that I trained to talk like me using Seq2Seq.,natural-language-processing
602,2020-04-23 00:36:02,DialoGPT: Toward Human-Quality Conversational Response Generation,Large-scale pre-training for dialogue.,natural-language-processing
605,2020-04-23 03:59:57,Upside Down Reinforcement Learning,Implementation of UDRL as outlined by Juergen Schmidhuber in https://arxiv.org/abs/1912.02875,other
608,2020-04-23 12:52:02,PyImageSearch,An online platform of blogs on Computer Vision and Deep Learning.,computer-vision
619,2020-04-23 16:55:27,Implementing Portrait Bokeh Mode using OpenCV and NumPy (Python),"Do you love the portrait mode in your smartphone? This code will help you do the same using OpenCV and NumPy! Detects the faces, asks if you want to blur them!",computer-vision
621,2020-04-23 18:17:12,MixNMatch,Multifactor Disentanglement and Encoding for Conditional Image Generation,computer-vision
622,2020-04-23 21:40:09,MT-Clinical BERT,Scaling Clinical Information Extraction with Multitask Learning,natural-language-processing
623,2020-04-24 00:30:02,medaCy,🏥 Medical Text Mining and Information Extraction with spaCy,natural-language-processing
632,2020-04-24 11:37:13,Lagrangian Neural Networks,"Trying to learn a simulation? Try Lagrangian Neural Networks, which explicitly conserve energy and may generalize better!",other
639,2020-04-24 20:51:18,ML Foundations and Methods for Precision Medicine and Healthcare,"This tutorial will discuss ideas from machine learning that enable personalization (useful for applications in education, retail, medicine and recsys).",other
643,2020-04-26 04:34:02,Albert-base for Sanskrit,Trained Albert-base from scratch on Sanskrit corpus of Wikipedia. I have also added a link to how to train your own Language model from scratch.,natural-language-processing
644,2020-04-26 05:42:37,Adversarial Latent Autoencoders,"Introducing the Adversarial Latent Autoencoder (ALAE), a general architecture that can leverage recent improvements on GAN training procedures.",computer-vision
652,2020-04-28 15:14:00,Optimal Transport and the Sinkhorn Transformer,Understand optimal transport and the Sinkhorn-Knopp algorithm before diving into the Sinkhorn Transformer.,natural-language-processing
653,2020-04-28 16:20:29,Semantic Graphs for Generating Deep Questions,"Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage. ",natural-language-processing
658,2020-04-28 21:34:00,Gutenberg Dialog,Build a dialog dataset from online books in many languages.,natural-language-processing
661,2020-04-29 02:41:24,Better NLP project,This is a wrapper program/library that encapsulates a couple of NLP libraries that are popular among the AI and ML communities.,natural-language-processing
663,2020-04-29 04:42:16,Recipes for building an open-domain chatbot,"Python framework for sharing, training and testing dialogue models, from open-domain chitchat to VQA (Visual Question Answering).",natural-language-processing
665,2020-04-29 10:46:20,Object-detection with multi-template matching,"This python package allows to perform object detection using one or a few template images, it provides a simpler alternative to deep-learning methods",computer-vision
667,2020-04-29 18:34:28,No Trump Social Chrome Plugin,An AI-driven Browser Extension to Replace Trump Pics with Puppies!,computer-vision
670,2020-04-29 19:35:22,Attribute2Font: Creating Fonts You Want From Attributes,Official PyTorch implementation of the Attribute2Font: Creating Fonts You Want From Attributes.,natural-language-processing
674,2020-04-30 17:52:55,YOLOv4: Optimal Speed and Accuracy of Object Detection,A minimal implementation of YOLOv4.,computer-vision
679,2020-05-01 16:17:32,Geometric and Relational Deep Learning,Videos from emerging fields of Graph Representation Learning and Geometric Deep Learning.,other
683,2020-05-01 16:35:06,TAPAS: Weakly Supervised Table Parsing via Pre-training,Using neural networks to find answers in tables.,natural-language-processing
686,2020-05-01 16:59:48,Jukebox: A Generative Model for Music,"We’re introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. ",natural-language-processing
687,2020-05-01 17:17:48,Exploratory Data Analysis of Time Series,"Exploratory Data Analysis of Time Series data in Python. It uses lot of the principles and concepts discussed in Prof. Hyndman's book. The focus is on understa
",other
688,2020-05-01 17:47:40,Gotchas of Transfer Learning for Image Classification,Discover the things you should care about while doing transfer learning for image classification. ,computer-vision
693,2020-05-02 05:05:44,SciTLDR: Extreme Summarization of Scientific Documents,A new automatic summarization task with high source compression requiring expert background knowledge and complex language understanding.,natural-language-processing
694,2020-05-02 15:29:06,BLINK: Better entity LINKing,Entity Linking python library that uses Wikipedia as the target knowledge base.,natural-language-processing
695,2020-05-02 21:33:31,Five Cool Python Libraries for Data Science,Python is a best friend for the majority of the Data Scientists. Libraries make their life simpler. I have come across five cool Python libraries while working ,natural-language-processing
700,2020-05-03 13:49:29,Fastai2 Vision Module,A detailed guide to using fastai2 Datablock API for common computer vision tasks,computer-vision
702,2020-05-03 20:19:10,Unsupervised Question Decomposition for Question Answering,"Decompose hard (multi-hop) questions into several, easier (single-hop) questions using unsupervised learning, and get better accuracy on multi-hop QA.",natural-language-processing
704,2020-05-04 11:58:27,Training Batch Norm and Only Batch Norm,Experiments with the ideas presented in https://arxiv.org/abs/2003.00152 by Frankle et al. ,computer-vision
707,2020-05-05 03:36:50,The Big Bad NLP Database,A collection of 400+ NLP datasets with papers included.,natural-language-processing
708,2020-05-05 03:51:53,POINTER: Constrained Text Generation,Constrained Text Generation via Insertion-based Generative Pre-training,natural-language-processing
712,2020-05-05 05:55:46,Covid-19: A-Geo-Statistical-Analysis,Analysis with the time series data available for various countries.,other
713,2020-05-05 07:13:49,Cognito : Data wrangling toolkit,Cognito is an exclusive python data preprocessing library and command-line utility that helps any developer to transform raw data into a machine-learning format,other
717,2020-05-05 14:46:57,Synthesizer: Rethinking Self-Attention in Transformer Models,The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required?,natural-language-processing
726,2020-05-06 01:10:55,ConvNets-TensorFlow2,Implementing a variety of popular and important CNN architectures,computer-vision
732,2020-05-06 04:20:43,StellarGraph - Machine Learning on Graphs,"State-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data.",other
733,2020-05-06 04:30:47,LandCover.ai,"Dataset for automatic mapping of buildings, woodlands and water from aerial imagery.",computer-vision
734,2020-05-06 04:33:15,Generating SOAP Notes from Doctor-Patient Conversations,Evaluate complete pipelines for leveraging these transcripts to train machine learning model to generate these notes.,natural-language-processing
741,2020-05-07 01:15:12,Zero-shot Neural Retrieval via Domain-targeted Synthetic Queries,Zero-shot learning for ad-hoc retrieval models that relies on synthetic query generation.,natural-language-processing
778,2020-05-07 21:28:34,Harry Potter and the Deep Learning Experiment,RNN built with TensorFlow to generate text based on Harry Potter's books.,natural-language-processing
783,2020-05-08 14:44:04,NeuralCook — Image2Ingredients and Cooking Recommendation,"Deep learning application to identify ingredients from cooking dishes images and recommend dishes to cook, given a set of ingredients.",natural-language-processing
788,2020-05-09 04:12:10,NER model for 40 languages trained with the new TFTrainer,This model is a fine-tuned XLM-Roberta-base over the 40 languages proposed in XTREME from Wikiann. ,natural-language-processing
791,2020-05-09 14:30:08,Pose Animator,Takes a 2D vector illustration and animates its containing curves in real-time based on the recognition result from PoseNet and FaceMesh.,computer-vision
792,2020-05-09 16:59:54,A Commit History of BERT and its Forks,What a commit history of version-controlled research papers could look like?,natural-language-processing
795,2020-05-10 04:51:17,U^2-Net,"The code for our newly accepted paper in Pattern Recognition 2020: ""U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection.""",computer-vision
796,2020-05-10 05:08:27,Age and Gender Estimation using Multi-Task CNN,Used a multi task CNN to predict the age group and gender of the person in the image.,computer-vision
797,2020-05-10 15:31:27,Data augmentation recipes in tf.keras image-based models,Learn about different ways of doing data augmentation when training an image classifier in tf.keras.,computer-vision
799,2020-05-11 00:40:49,Injecting Inductive Bias in Graph Neural Networks (MIT talk),Equivariant Mesh Neural Networks and Neural Augmented (Factor) Graph Neural Networks.,other
800,2020-05-11 00:44:10,Feature Stores for ML,List of production ML groups and their open-source feature store architectures.,mlops
803,2020-05-11 02:13:32,Image Semantic Segmentation of UAV mining area based on Deeplabv3,"Data: UAV mining area image
Tools: PyTorch
Frame: Deeplabv3
Semantic Segmentation ",computer-vision
820,2020-05-11 14:19:18,A Comprehensive Survey on Graph Neural Networks,A Comprehensive Survey on Graph Neural Networks.,other
821,2020-05-11 15:03:57,Hidden Technical Debt in Machine Learning Systems,"Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems. ",mlops
822,2020-05-11 15:10:09,In-Domain GAN Inversion for Real Image Editing,"We propose an in-domain GAN inversion method, which faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful.",computer-vision
825,2020-05-11 23:07:39,Neural Networks for NLP (CMU CS 11-747),"This class will start with a brief overview of neural networks, then spend the majority of the class demonstrating how to apply neural networks to language.",natural-language-processing
826,2020-05-12 03:02:02,DANet PyTorch,A Pytorch implementation of Dual Attention Network for Scene Segmentation,computer-vision
828,2020-05-12 05:04:58,BART version of closed-book QA,"This is a BART version of sequence-to-sequence model for open-domain QA in a closed-book setup, based on PyTorch and Huggingface's Transformers.",natural-language-processing
829,2020-05-12 05:07:35,Unsupervised Reinforcement Learning,Lecture on unsupervised reinforcement learning by Sergey Levine. Originally prepared for AAMAS 2020.,other
831,2020-05-13 02:24:24,CCNet_PyTorch,A PyTorch Implementation of CCNet: Criss-Cross Attention for Semantic Segmentation,computer-vision
832,2020-05-13 04:22:09,Image segmentation in 2020,"Architectures, Losses, Datasets, and Frameworks",computer-vision
833,2020-05-13 04:27:08,Plan2Explore: Plan to Explore via Self-Supervised World Models,A self-supervised reinforcement learning agent that tackles task-specific and the sample efficiency challenges.,other
835,2020-05-13 04:39:31,Toward Better Storylines with Sentence-Level Language Models,We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives.,natural-language-processing
836,2020-05-13 04:43:57,Epipolar Transformers,"Differentiable ""epipolar transformer"", which enables the 2D detector to leverage 3D-aware features to improve 2D pose estimation.",computer-vision
840,2020-05-13 05:03:33,Machine Learning on Graphs: A Model and Comprehensive Taxonomy,We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.,other
841,2020-05-13 05:10:58,BLEURT: Learning Robust Metrics for Text Generation,A metric for Natural Language Generation based on transfer learning.,natural-language-processing
842,2020-05-13 13:20:07,Identifying Brain Tumor from MRI images using FastAI -DynamicUnet,"To use FASTAI unet learner to identify tumours from MRI of Brain, logging loss metrics in Neptune AI logger and compare the results after hyperparameter tuning.",computer-vision
847,2020-05-13 22:53:36,HuggingTweets,Tweet Generation with Huggingface.,natural-language-processing
849,2020-05-13 22:59:38,Top Down Introduction to BERT with HuggingFace and PyTorch,I will also provide some intuition into how BERT works with a top down approach (applications to algorithm).,natural-language-processing
850,2020-05-13 23:02:29,Transformers from Scratch,"Attempt to explain directly how modern transformers work, and why, without some of the historical baggage.",natural-language-processing
852,2020-05-14 07:11:26,Scene Classification using Pytorch and Fast.ai,The objective is to classify Multi-label images using deep learning. Here I have used Fast.ai library for implementing the model. ,computer-vision
855,2020-05-14 12:32:20,Fake new detection Pytorch,Fake News Detection by Learning Convolution Filters through Contextualized Attention.,natural-language-processing
857,2020-05-14 14:25:11,FastHugs: Sequence Classification with Transformers and Fastai,Fine-tune a text classification model with HuggingFace 🤗 transformers and fastai-v2.,natural-language-processing
858,2020-05-14 14:35:37,Open-Dialog Chatbots for Learning New Languages,A tutorial for automatically generate code comments using Deep Learning.,natural-language-processing
860,2020-05-14 17:35:04,Electra,ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators,natural-language-processing
862,2020-05-14 19:13:59,DQN In Pytorch Livestream Series,I'm doing a series of streams about reinforcement learning (starting from Q learning) focused on showing the work in as much detail as possible (e.g. debugging),other
863,2020-05-15 04:24:58,S2IGAN: Speech-to-Image Generation via Adversarial Learning,A speech-to-image generation (S2IG) framework is proposed which translates speech descriptions to photo-realistic images without using any text information.,computer-vision
864,2020-05-15 13:04:19,Twitter Sentiment Analysis,"This project is based on Natural Language processing (NLP), in this we do sentiment analysis(i.e, how much it is positive or negative) of tweets of any account.",natural-language-processing
866,2020-05-15 13:51:56,HuggingFace nlp library,"nlp is a lightweight and extensible library to easily share and load dataset and evaluation metrics, already providing access to ~100 datasets and ~10 evaluatio",natural-language-processing
868,2020-05-15 14:07:47,RXNMapper: Unsupervised Attention-Guided Atom-Mapping,The atom-mapping information was learned by an ALBERT model trained in an unsupervised fashion on a large dataset of chemical reactions.,natural-language-processing
869,2020-05-15 14:08:12,ICLR 2020 Trends: Better & Faster Transformers for NLP,A summary of promising directions from ICLR 2020 for better and faster pretrained tranformers language models. ,natural-language-processing
875,2020-05-15 22:53:58,Differentiable Reasoning over Text,We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB).,natural-language-processing
877,2020-05-16 02:42:32,Semi-supervised image classification with GANs,"Shows how to perform semi-supervised image classification with GANs. The cover image is from Chapter 7, GANs in Action.",computer-vision
879,2020-05-16 10:57:53,HighRes-net: Multi-Frame Super-Resolution of satellite imagery,"Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition.",computer-vision
880,2020-05-16 11:50:31,How Deep Is Your Love For Transfer Learning In NLP?,A review of NLP research,natural-language-processing
881,2020-05-16 13:32:51,Time Series Forecasting with TensorFlow.js,Machine learning is becoming increasingly popular these days and a growing number of the world’s population see it is as a magic crystal ball: predicting when a,other
882,2020-05-16 13:35:31,Phrases extraction and D3 Wordcloud,100% JavaScript solution to extracting phrases from text and display key points in a beautiful D3 wordcloud.,natural-language-processing
883,2020-05-16 13:37:44,Reinforcement Learning Tic Tac Toe with Value Function,"A reinforcement learning algorithm for agents to learn the tic-tac-toe, using the value function
",other
884,2020-05-16 13:40:07,Build a Textual Similarity Web App with TensorFlow.js,Have you wondered how search engines understand your queries and retrieve relevant results? How chatbots extract your intent from your questions and provide the,natural-language-processing
890,2020-05-16 19:51:33,cyBERT: Applying BERT to Windows event logs,"This blog shows how interpreting cybersecurity logs as a natural language, improving upon the standard regex-based parsing of log data.",natural-language-processing
892,2020-05-17 02:08:12,DPOD: Pose Estimator,PyTorch recreation of a SOTA 6D Pose estimation research paper. ,computer-vision
893,2020-05-17 04:44:04,ESTorch,ESTorch is an Evolution Strategy Library build around PyTorch.,other
894,2020-05-17 04:47:40,"A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS ","Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS).",natural-language-processing
900,2020-05-17 08:14:24,A Visual Survey of Data Augmentation in NLP,An extensive overview of text data augmentation techniques for Natural Language Processing,natural-language-processing
901,2020-05-17 09:57:38,DoYouEvenLearn,Essential Guide to keep up with AI/ML/DL/CV,computer-vision
902,2020-05-18 00:57:27,Differentiable Adaptive Computation Time for Visual Reasoning ,"DACT, a new algorithm for achieving adaptive computation time that, unlike existing approaches, is fully differentiable. ",natural-language-processing
903,2020-05-18 11:15:12,Semixup: In- and Out-of-Manifold Regularization,Semixup is a semi-supervised learning method based on in/out-of-manifold regularization.,computer-vision
905,2020-05-18 14:40:51,Deep Reinforcement Learning for Supply Chain & Price Optimization,Explore how deep reinforcement learning methods can be applied in several basic supply chain and price management scenarios.,other
907,2020-05-18 14:53:33,TextAttack,A Python framework for building adversarial attacks on NLP models.,natural-language-processing
913,2020-05-19 03:19:59,aitextgen,A robust Python tool for text-based AI training and generation using GPT-2.,natural-language-processing
914,2020-05-19 03:25:11,How Hugging Face achieved a 2x performance boost for QA,Question Answering with DistilBERT in Node.js,natural-language-processing
918,2020-05-19 22:36:09,Accelerate your NLP pipelines using Hugging Face and ONNX,How the ONNX Runtime team and Hugging Face are working together to address challenges in training and deployment of Transformer models.,natural-language-processing
920,2020-05-20 02:35:11,Attentron,Few-shot text-to-speech exploiting attention-based variable length embedding,natural-language-processing
921,2020-05-20 02:39:09,Torch Points3D,Pytorch framework for doing deep learning on point clouds.,computer-vision
922,2020-05-20 07:23:50,NLP Model Selection ,NLP model selection guide to make it easier to select models. This is prescriptive in nature and has to be used with caution.,natural-language-processing
925,2020-05-20 16:20:28,Model-Agnostic Meta-Learning for Reinforcement Learning with TF2,Reimplementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.,other
927,2020-05-21 03:16:17,FashionBERT,Text and image matching with adaptive loss for cross-modal retrieval.,natural-language-processing
934,2020-05-21 03:45:38,📈 Automated Time Series Forecasting,This data app uses Facebook's open-source Prophet library to automatically forecast values into the future. ,other
935,2020-05-21 14:22:01,"Look inside the workings of ""Label Smoothing""","This blog post describes how and why does ""trick"" of label smoothing improves the model accuracy and when should we use it ",computer-vision
938,2020-05-22 01:01:32,Content and Style Disentanglement for Artistic Style Transfer,Hi-Res style transfer and interpolation between styles,computer-vision
939,2020-05-22 03:08:40,Time Series Classification Using Deep Learning,"In this article, I will introduce you to a new package called timeseries for fastai2 that I lately developed. ",other
940,2020-05-22 03:16:29,TAO: A Large-Scale Benchmark for Tracking Any Object,"A diverse dataset for Tracking Any Object (TAO) consisting of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on ",computer-vision
941,2020-05-22 03:21:10,BiT: Exploring Large-Scale Pre-training for Compute,"We are excited to share the best BiT models pre-trained on public datasets, along with code in TF2, Jax, and PyTorch. ",computer-vision
947,2020-05-22 13:34:30,Self Driving Car,This project is a demonstration of a working model of self driving car 🚗🚗 identifying and following lanes using powerful computer vision 🕶🕶 algorithms.,computer-vision
948,2020-05-22 13:39:15,Plant Disease Detection,This website help you to detect disease in your plant🌳 based to the plant's leaf🍃 image,computer-vision
951,2020-05-23 03:19:00,YoloV3 implementation in keras and tensorflow 2.2,YoloV3 Real Time Object Detector in tensorflow 2.2.,computer-vision
952,2020-05-23 03:22:11,Face Mask Detector,A simple Streamlit frontend for face mask detection in images using a pre-trained Keras CNN model + OpenCV and model interpretability.,computer-vision
957,2020-05-23 09:18:52,Colbert AI,Colbert AI is a Deep Learning Language Model that generates text in the style of Stephen Colbert's famous monologues.,natural-language-processing
961,2020-05-23 16:01:21,How to Build Robust Embeddings for Visual Similarity Tasks,This repository I package a bunch of tips and tricks to efficiently train deep learning models in computer vision,computer-vision
962,2020-05-24 00:09:28,Basic ML Algorithms from scratch.,Implement basic Machine Learning Algorithms from scratch in python.,natural-language-processing
963,2020-05-24 03:13:28,Build your first data warehouse with Airflow on GCP,What are the steps in building a data warehouse? What cloud technology should you use? How to use Airflow to orchestrate your pipeline?,mlops
966,2020-05-24 10:24:03,Building an Intelligent Twitter Bot,The volume of information going through Twitter per day makes it one of the best platforms to get information on any subject of interest. ,natural-language-processing
968,2020-05-24 16:40:46,Self Supervised Representation Learning in NLP,An overview of self-supervised pretext tasks in Natural Language Processing,natural-language-processing
970,2020-05-24 20:01:29,Job Classification,"Job Classification done using Techniques of NLP and ML.
Dataset used from Kaggle of Indeeed job posting.",natural-language-processing
972,2020-05-25 03:23:16,Next Word Prediction,Using transformers to predict next word and predict <mask> word.,natural-language-processing
974,2020-05-25 03:28:32,PixelLib,Pixellib is a library for performing segmentation of images. ,computer-vision
978,2020-05-25 05:53:46,TensorFlow.js - Gesture Controlled 2048,Gesture Controlled 2048 built with TensorFlow.js,computer-vision
979,2020-05-25 11:04:50,Taxi Demand Prediction NewYorkCity,Predict the number of pickups as accurately as possible for each region in a 10 -min interval.,other
980,2020-05-25 14:52:17,Super-BPD for Fast Image Segmentation,"We propose direction-based super-BPD, an alternative to superpixel, for fast generic image segmentation, achieving state-of-the-art real-time result.",computer-vision
986,2020-05-26 03:47:15,Neural Topological SLAM for Visual Navigation,Topological representations for space that effectively leverage semantics and afford approximate geometric reasoning.,computer-vision
987,2020-05-26 13:16:48,Zero To One For NLP,A collection of all resources for learning NLP,natural-language-processing
989,2020-05-26 17:17:14,NLP for Developers: Shrinking Transformers | Rasa,"In this video, Rasa Senior Developer Advocate Rachael will talk about different approaches to make transformer models smaller.",natural-language-processing
993,2020-05-27 05:26:33,DETR: End-to-End Object Detection with Transformers,A new method that views object detection as a direct set prediction problem. ,computer-vision
997,2020-05-28 03:20:06,AutoSweep: Recovering 3D Editable Objects from a Single Photo,Fully automatic framework for extracting editable 3D objects directly from a single photograph.,computer-vision
1000,2020-05-28 03:33:52,CMU LTI Low Resource NLP Bootcamp 2020,A low-resource natural language and speech processing bootcamp held by the Carnegie Mellon University Language Technologies Institute in May 2020.,natural-language-processing
1007,2020-05-28 21:30:37,Humour.ai : Language Model that can crack Jokes,"A Language model that can make you laugh. Humour.ai model tries to
complete a sentence in a humourous way given some input words. ",natural-language-processing
1008,2020-05-29 02:28:53,face mask detection ,detects whether a person wearing a mask or not,computer-vision
1009,2020-05-29 02:47:06,Train ALBERT for NLP with TensorFlow on Amazon SageMaker,"To train BERT in 1 hour, we efficiently scaled out to 2,048 NVIDIA V100 GPUs by improving the underlying infrastructure, network, and ML framework. ",natural-language-processing
1010,2020-05-29 02:51:39,GPT-3: Language Models are Few-Shot Learners,"We show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior SOTA.",natural-language-processing
1013,2020-05-29 03:06:41,Guided Uncertainty-Aware Policy Optimization,Combining learning and model-based strategies for sample-efficient policy learning.,other
1018,2020-05-29 08:09:04,GOTURN-PyTorch,"PyTorch implementation of ""Learning to Track at 100 FPS with Deep Regression Networks""",computer-vision
1020,2020-05-29 09:54:04,Applying Modern Best Practices to Autoencoders,This project applies best modern practices found in other areas of image research to autoencoders. Comparing models from other areas of image research.,computer-vision
1021,2020-05-29 10:33:26,Sentiment analysis ,"Sentiment analysis by combining three dataset amazon,yelp, IMDb reviews to train our,model to classify if a comment is negative or positive denoted by 0 and 1.",natural-language-processing
1022,2020-05-29 13:27:20,The designer - gpt2 bot that talks about UX Design,"This twitter profile spits out thoughts on design and development. Trained with hundreds of Books on UX design and Front end development, it has opinions.",natural-language-processing
1024,2020-05-29 14:15:30,Sentiment Classification for UtaPass & KKBOX Reviews,Text classification for reviews of UtaPass & KKBOX using different deep learning models.,natural-language-processing
1025,2020-05-29 14:18:59,Forex Prediction,Using neural networks to predict movement of forex direction.,natural-language-processing
1026,2020-05-29 14:24:07,Lyrics-Based Music Genre Classifier,"Classify the genre (Rock, Pop, Hip-Hop, Not Available, Metal, Other, Country, Jazz, Electronic, R&B, Indie, Folk) of the song by its lyrics.",natural-language-processing
1028,2020-05-29 14:39:16,ARBML,"Implementation of many Arabic NLP and ML projects. Providing real time experience using many interfaces like web, command line and notebooks.",natural-language-processing
1035,2020-05-29 16:11:11,Zero Shot Topic Classification,Bart with a classification head trained on MNLI.,natural-language-processing
1045,2020-05-30 01:35:24,Illustrated Guide to Transformers: Step by Step Explanation,"In this post, we’ll focus on the one paper that started it all, “Attention is all you need”.",natural-language-processing
1046,2020-05-30 01:39:25,Illustrated Guide to Transformers,A component by component breakdown analysis.,natural-language-processing
1055,2020-05-30 09:02:27,Automatic-Face-Detection-Annotation-and-Preprocessing,"Automatically detect , annotate , collect the coordinates , convert to csv and to tfrecord",computer-vision
1058,2020-05-30 09:43:39,SmartFeed.ai,NLP Based Article Recommendation System ,natural-language-processing
1059,2020-05-30 10:50:55,Wheat Detection 🌾,This is a project for detecting and creating bounding box of wheat heads 🌾.,computer-vision
1068,2020-05-30 18:20:40,Effects of News Sentiments on Stock Predictions,Project is based on the Natural Language Processing technique called Sentiment Analysis. Stock market and news related to it as the subject of analysis.,natural-language-processing
1069,2020-05-30 20:04:49,NLP News Category,The objective of this repository is to create a NLP bot for when you give the robot the headline of the news and a short description it will return the genre.,natural-language-processing
1070,2020-05-30 20:06:48,AI Debate Master,"Created and deployed a bot made to debate with a human on any
given topic. Employed a Doc2Vec model using Gensim library in Python",natural-language-processing
1075,2020-05-31 04:44:27,Zero-Shot Learning for Text Classification,"A visual summary of “Train Once, Test Anywhere” paper for zero-shot text classification",natural-language-processing
1080,2020-05-31 05:23:23,Dash DETR Detection App,A User Interface for DETR built with Dash. 100% Python.,computer-vision
1081,2020-05-31 05:28:53,AI Basketball Analysis,🏀 AI web app and API to analyze basketball shots and shooting pose. ,computer-vision
1083,2020-05-31 08:20:06,Reverse Image Search,Have you ever wondered how google image search works or How amazon can retrieve products similar to the image that we upload in the app/site? To achieve this ta,computer-vision
1084,2020-05-31 08:22:45,Beginner’s guide to Machine Learning Model Deployment,Are you a beginner in the field of machine learning and wondering how to bring your project to live. I'm was in the same situation when I started learning ML. M,mlops
1093,2020-05-31 17:39:22,MedicalZoo PyTorch,A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation,computer-vision
1094,2020-05-31 19:11:28,Paraphrase Any Question with T5 (Text-To-Text Transformer),"Given a question, generate paraphrased versions of the question with T5 transformer. Pretrained model and training script provided.",natural-language-processing
1100,2020-06-01 05:56:43,Movie Recommendation System,This is a web app which recommends movies based on their plots found on IMDb.,natural-language-processing
1104,2020-06-01 10:02:09,Convnet Galaxy Morphology Classifier,Classify galaxies from Hubble Tuning Fork using Convnet. ,computer-vision
1107,2020-06-01 14:52:29,2nd Place Solution to Ship Identification Hackathon ,The problem statement was to identify the type of ship from photos taken from the survey boats. The hackathon was organized by Analytics Vidhya.,computer-vision
1110,2020-06-01 16:44:55,Deep learning Architecture: AlexNet,Explaining network architecture for AlexNet,computer-vision
1111,2020-06-01 18:13:26,Movement Pruning: Adaptive Sparsity by Fine-Tuning,"We propose the use of movement pruning, a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning.",natural-language-processing
1112,2020-06-01 18:57:31,Document search engine,NLP based search engine for single page pdf files.,natural-language-processing
1115,2020-06-01 21:07:53,Softbot design with WANNS,"Soft robots are robots built from highly compliant materials, similar to those found in living organisms. This project explored CPPNs and WANNs to design them",other
1121,2020-06-02 05:07:17,Motion2Vec,Semi-Supervised Representation Learning from Surgical Videos,computer-vision
1122,2020-06-02 05:10:18,Machine Learning: Tests and Production,Best practices for testing ML-based systems.,mlops
1130,2020-06-02 11:51:38,Generate True or False questions from any content,"Automatically generate “True or False” questions like the ones you see in school textbooks using OpenAI GPT2, Sentence BERT, and Berkley parser",natural-language-processing
1131,2020-06-02 13:41:32,Sized Fill-in-the-blank or Multi Mask filling with RoBERTa,Sized fill-in-the-blank or conditional text filling is the idea of filling missing words of a sentence with the most probable choice of words.,natural-language-processing
1132,2020-06-02 14:56:10,T5 for Sentiment Span Extraction,Exploring how T5 works and applying it for sentiment span extraction.,natural-language-processing
1133,2020-06-02 14:58:58,Getting Started with Time Series analysis using Pandas,An introductory guide to get started with the Time Series datasets in Python,other
1135,2020-06-02 15:06:34,Melanoma Detection with Pytorch,"In this video, I show you how you can build a deep learning model to detect melanoma with a very high accuracy.",computer-vision
1139,2020-06-02 19:53:37,"RoBERTa → Longformer: Build a ""Long"" Version of Pretrained Models",This notebook replicates the procedure descriped in the Longformer paper to train a Longformer model starting from the RoBERTa checkpoint. ,natural-language-processing
1145,2020-06-03 01:51:14,Learning Dexterity End-to-End,We trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.,other
1148,2020-06-03 02:28:20,A Practical guide to building a conversational chatbot,Building a Chatbot from scratch using Keras and NLTK library for a customer service company,natural-language-processing
1151,2020-06-03 07:25:27,Web Mining and Information theory,"Mining the Web and playing with Natural Language processing. Implementing Information retrieval System tasks. Going towards the NLP and Performing Machine Learning algorithms. Through these codes and problems, I have understood the information retrieval process of any search engine. These are very useful problems towards sentiment analysis.",natural-language-processing
1162,2020-06-03 22:05:30,Deep Q-Network on Space Invaders. ,This is a PyTorch implementation of a Deep Q-Network agent trained to play the Atari 2600 game of Space Invaders.,other
1165,2020-06-04 03:53:43,YOLOv4,A TensorFlow 2.0 implementation of YOLOv4: Optimal Speed and Accuracy of Object Detection.,computer-vision
1166,2020-06-04 03:55:53,Acme: A Research Framework for Reinforcement Learning,A library of reinforcement learning components and agents.,other
1176,2020-06-04 09:10:07,doc2vec Paragraph Embeddings for Text Classification,Text classification model which uses gensim's Doc2Vec for generating paragraph embeddings and scikit-learn Logistic Regression for classification. ,natural-language-processing
1178,2020-06-04 12:19:52,Machine Learning with Fastai,"The fastai library is based on research into deep learning best practices undertaken at fast.ai, and includes support for Vision, Text, tabular and Collab",computer-vision
1180,2020-06-04 14:58:19,The Transformer … “Explained”?,"An intuitive explanation of the Transformer by motivating it through the lens of CNNs, RNNs, etc.",natural-language-processing
1181,2020-06-04 16:28:24,TensorflowTTS: Real-Time SOTA Speech Synthesis for Tensorflow 2.0,"TensorflowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron2, Melgan, FastSpeech.",natural-language-processing
1185,2020-06-04 22:36:31,PyTorch Transformers Tutorials,"A set of annotated Jupyter notebooks, that give user a template to fine-tune transformers model to downstream NLP tasks such as classification, NER etc. ",natural-language-processing
1192,2020-06-05 04:28:52,BERT Summarization,This folder contains colab notebooks that guide you through the summarization by BERT and GPT-2 to play with your data.,natural-language-processing
1194,2020-06-05 04:35:14,Divide Hugging Face Transformers Training Time By 2,Reducing training time helps to iterate more in a fixed budget time and thus achieve better results.,natural-language-processing
1199,2020-06-05 15:39:56,How NLP has evolved for Financial Sentiment Analysis,Do we still need humans to read boring financial statements?,natural-language-processing
1202,2020-06-05 17:51:33,The NLP Pandect - All NLP resources in one place,The NLP Pandect was created to help you find almost anything related to Natural Language Processing that is available online.,natural-language-processing
1203,2020-06-05 18:18:18,Summary of 🤗 Transformers Models,A high-level summary of the differences between each model in HuggingFace's Transformer library.,natural-language-processing
1204,2020-06-05 22:56:38,Snaked: Classifying Snake Species using Images,Proof of concept that it is possible to identify snake species and whether poisonous from photographs (PyTorch code/model with Android app),computer-vision
1211,2020-06-06 15:13:13,Literate Lamp: Answering Question with Common Sense,We study the problem of answering questions that require common sense to be answered using Transformer-based models and the ConceptNet knowledge base.,natural-language-processing
1215,2020-06-06 19:00:39,Pytorch Faster RCNN,Fine Tune Faster RCNN in pytorch for your task.,computer-vision
1222,2020-06-07 04:34:58,Paragraph Summarizer,Uses the extractive way of summarizing the text by finding the score and ranking it.,natural-language-processing
1223,2020-06-07 04:39:32,Leafy: Plant Leaf Classifier,The sequential model trained on images from the leafsnap.com,computer-vision
1236,2020-06-07 21:03:31,"COVID-Q: A Dataset of 1,690 Questions about COVID-19","This dataset consists of COVID-19 questions which have been annotated into a broad category (e.g. Transmission, Prevention) and a more specific class such that ",natural-language-processing
1237,2020-06-08 03:43:45,Keras notifications on Slack!,Get slack notifications of your model's training progress when training with Keras (or tf.keras),computer-vision
1239,2020-06-08 07:05:15,Zero-shot Text Classification With Generative Language Models,An overview of a text generation approach to zero-shot text classification with GPT-2,natural-language-processing
1241,2020-06-08 08:25:01,Funnel-Transformer: Filtering out Sequential Redundancy,Funnel-Transformer is a self-attention model that gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost.,natural-language-processing
1243,2020-06-08 08:39:34,Timeseries Anomaly Detection using an Autoencoder,Detect anomalies in a timeseries using an Autoencoder.,other
1246,2020-06-08 09:47:02,Fairseq-tagging,"a Fairseq fork for sequence tagging/labeling tasks
",natural-language-processing
1249,2020-06-08 16:59:01,Know-Corona : Kaggle COVID-19 Open Research Dataset Challenge (CO,"NLP/state-of-the-art language model (BERT) based Question & Answering pipeline to answer all task questions after analyzing articles abstract of COVID-19, SARS-",natural-language-processing
1251,2020-06-08 18:38:49,Automatic Asset Classification,This project aims to automate the task of labelling images of flood defence assets as well as clustering images to find possibly better groupings.,computer-vision
1255,2020-06-09 01:50:33,TransformerTTS,🤖💬 Transformer TTS: Implementation of a non-autoregressive Transformer based neural network for text to speech.,natural-language-processing
1257,2020-06-09 01:58:48,How Big Should My Language Model Be?,Tool to explore language model training and optimize the compute costs.,natural-language-processing
1258,2020-06-09 02:04:49,MSeg: A Composite Dataset for Multi-domain Semantic Segmentation,A composite dataset that unifies semantic segmentation datasets from different domains.,computer-vision
1259,2020-06-09 02:11:15,Network Fusion for Content Creation With Conditional Inns,"We present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them.",computer-vision
1260,2020-06-09 02:14:59,Advanced Deep Learning for Computer Vision (ADL4CV),"The Visual Computing Group offers a variety of lectures and seminars on a regular basis, covering hot areas in computer graphics, vision, and machine learning.",computer-vision
1272,2020-06-10 05:13:41,Linformer: Self-Attention with Linear Complexity,We demonstrate that the self-attention mechanism can be approximated by a low-rank matrix.,natural-language-processing
1274,2020-06-10 05:21:00,Getting Machine Learning to Production,"Machine learning is hard and there are a lot, a lot of moving pieces.",mlops
1275,2020-06-10 05:24:07,Exploration Strategies in Deep Reinforcement Learning,Exploitation versus exploration is a critical topic in reinforcement learning. This post introduces several common approaches for better exploration in Deep RL.,other
1278,2020-06-10 12:50:41,Automatically Generate Multiple Choice Questions (MCQs) ,"Automatically Generate Multiple Choice Questions (MCQs) from any content with BERT Summarizer, Wordnet, and Conceptnet",natural-language-processing
1287,2020-06-10 18:27:24,BERT Loses Patience: Fast and Robust Inference with Early Exit,"Patience-based Early Exit, a inference method that can be used as a plug-and-play technique to simultaneously improve the efficiency of a pretrained LM.",natural-language-processing
1298,2020-06-11 04:18:27,PEGASUS: a SOTA model for Abstractive Text Summarization,A State-of-the-Art Model for Abstractive Text Summarization.,natural-language-processing
1301,2020-06-11 04:29:24,Big GANs Are Watching You, We demonstrate that object saliency masks for GAN-produced images can be obtained automatically with BigBiGAN.,computer-vision
1309,2020-06-11 19:04:31,Sentiment Analysis on News Article,Used Twitter API to extract news-related tweets. Did some preprocessing and then calculated the tweets' polarity.,natural-language-processing
1310,2020-06-11 20:30:38,GPT-3 Language Model: A Technical Overview,"Technical details of the GPT-3 model, training, inference and what to expect next. ",natural-language-processing
1312,2020-06-11 20:37:47,OpenAI API,API for accessing new AI models developed by OpenAI.,natural-language-processing
1320,2020-06-12 04:17:08,Implementation of a Contextual Chatbot in PyTorch,Simple chatbot implementation with PyTorch.,natural-language-processing
1325,2020-06-12 11:06:34,Author Identification using Doc2Vec,Web app of an author identification model trained on PAN 2012 dataset and Kaggle's Spooky Authorship Dataset,natural-language-processing
1329,2020-06-12 12:44:18,Training game agents with supervised learning,This is a continuing research project trying find ways to learn complex tasks such as games without using Reinforcement Learning.,other
1371,2020-06-13 17:16:07,Baymax - ChatBot,"Baymax Chatbot is a part of my summer training program @AdHoc Networks, Jaipur.
A chatbot that allows user to signup and login to maintain their record. When c",natural-language-processing
1372,2020-06-13 17:21:43,How to Evaluate Longformer on TriviaQA using NLP,We will evaluate a pretrained LongformerForQuestionAnswering model on the validation dataset of TriviaQA.,natural-language-processing
1374,2020-06-13 17:28:13,Extracting Structured Data from Templatic Documents,"Automatically extract data from structured documents—invoices, receipts, etc.—with the potential to streamline many business workflows.",computer-vision
1392,2020-06-13 20:58:33,StackOver Flow Data Analysis,"Analysing certain aspects of the stack overflow data and creating ""Tag Predictor"" which predicts tag based on the post posted. ",natural-language-processing
1398,2020-06-14 05:51:06,Super-resolution Variational Auto-Encoders,VAE with RealNVP prior and Super-Resolution VAE in PyTorch.,computer-vision
1399,2020-06-14 05:57:16,Video object grounding,Video object grounding using semantic roles in language description. ,computer-vision
1418,2020-06-14 17:43:34,Short Notes on Behavior Regularized Offline RL,Blog Article on Behavior Regularized Offline Reinforcement Learning by Yifan Wu et al. (2019),other
1423,2020-06-14 22:10:57,Entity Embedding with LSTM for Time-Series,"Demonstration of using LSTM for forecasting with structured time-series data, containing categorical and numerical features.",other
1424,2020-06-15 02:27:55,Why We Switched from Flask to FastAPI for Production ML,The most popular tool isn’t always the best.,mlops
1425,2020-06-15 02:31:48,Building a Captcha OCR in TF2.0,A Kaggle notebook showcasing the use of an Endpoint layer for CTC loss function used for building a Captcha Reader in TensorFlow.,computer-vision
1427,2020-06-15 02:40:48,101 Ways to Solve Search - Dair AI ft. Pratik Bhavsar,A comprehensive overview of explaining how NLP is used for search.,natural-language-processing
1438,2020-06-15 11:06:35,Multimodal Meme Classification,UNITER has given state of the art results in various image-text related problems. This project aims at finetuning UNITER to solve Hateful memes challenge,computer-vision
1453,2020-06-16 01:32:49,Interpretable Machine Learning for Computer Vision,"Recent progress we made on visualization, interpretation, and explanation methodologies for analyzing both the data and the models in computer vision.",computer-vision
1455,2020-06-16 02:32:53,Predicting Unintentional Action in Video,"We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recognizing, localizing, and anticipating its onset. ",computer-vision
1457,2020-06-16 02:46:25, Synthesizing High-Resolution Images with StyleGAN2,"Developed by NVIDIA Researchers, StyleGAN2 yields state-of-the-art results in data-driven unconditional generative image modeling.",computer-vision
1458,2020-06-16 02:51:13,PIFuHD: High-Resolution 3D Human Digitization ,"This repository contains a pytorch implementation of ""Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization"".",computer-vision
1460,2020-06-16 03:21:07,Instance Shadow Detection,Instance shadow detection aims to find shadow instances paired with object instances.,computer-vision
1461,2020-06-16 03:24:02,Detectron2,FAIR's next-generation platform for object detection and segmentation.,computer-vision
1473,2020-06-16 22:37:58,tslearn,A machine learning toolkit dedicated to time-series data.,other
1475,2020-06-16 22:45:15,PyTorch3D,FAIR's library of reusable components for deep learning with 3D data.,computer-vision
1476,2020-06-16 22:48:45,Course Review - Causal Inference,Types of understanding that causal inference researchers value.,other
1478,2020-06-16 22:56:31,Unsupervised Learning of Probably Symmetric Deformable 3D Objects,"A method to learn 3D deformable object categories from raw single-view images, without external supervision.",computer-vision
1480,2020-06-16 23:06:13,A Guide to Natural Language Processing With AllenNLP,basics of using AllenNLP,natural-language-processing
1482,2020-06-17 12:12:03,Real Time Object Detection using CNN YOLO,"This project is done on real time object detection using a deep learning object detection algorithm i.e., YOLO.",computer-vision
1483,2020-06-17 14:38:33,Short Notes on Model-Based Offline Reinforcement Learning (MOReL),Blog article on Model-Based Offline Reinforcement Learning (MOReL) paper by Rahul Kidambi & Aravind Rajeswaran et al.,other
1491,2020-06-18 00:04:34,Image GPT: Generative Pretraining from Pixels, Transformers trained on pixel sequences can generate coherent image completions and samples.,computer-vision
1492,2020-06-18 00:06:53,Q*BERT,Agents that build knowledge graphs and explore textual worlds by asking questions.,natural-language-processing
1499,2020-06-18 13:41:39,History of Language Models - Alec Radford,A quick history of language models,natural-language-processing
1502,2020-06-18 19:45:49,Generate Boolean (Yes/No) Questions From Any Content ,Question generation algorithm trained on the BoolQ dataset using T5 text-to-text transformer model.,natural-language-processing
1504,2020-06-19 06:19:25,Fast Neural Style Transfer (feed-forward method) ⚡💻 + 🎨 = ❤️,This repo contains a concise PyTorch implementation of the original feed-forward NST paper.,computer-vision
1505,2020-06-19 06:22:56,Diverse Image Generation via Self-Conditioned GANs,A simple but effective unsupervised method for generating realistic & diverse images using a class-conditional GAN model without using manually annotated class.,computer-vision
1506,2020-06-19 06:26:17,Using GitHub Actions for MLOps & Data Science,A collection of resources on how to facilitate Machine Learning Ops with GitHub.,mlops
1519,2020-06-20 05:40:46,Image and Bounding Box Annotation Slicer,This easy-to-use library slices (also resizes) images and its bounding box annotations into tiles of specific sizes or any arbitrary number of equal parts. ✂️,computer-vision
1525,2020-06-20 16:21:38,Huggingtweets,This is a streamlit app built around the huggingtweets project. I fine-tune a pre-trained gpt2 model to tweet like a user given twitter handle. ,natural-language-processing
1528,2020-06-20 22:06:48,The Future of Computer Vision is Self-Supervised Learning,Talk by Yann Lecun on the applications of self-supervised learning on computer vision during CVPR 2020.,computer-vision
1529,2020-06-20 22:11:14,Using Selective Attention in Reinforcement Learning Agents,"In this work, we establish that self-attention can be viewed as a form of indirect encoding, which enables us to construct highly parameter-efficient agents.",other
1539,2020-06-21 12:45:42,A Visual Guide to FastText Word Embeddings,A deep-dive into how FastText enriches word vectors with sub-word information ,natural-language-processing
1542,2020-06-21 20:46:12,Autocoder - Finetuning GPT-2 for Auto Code Completion,"A basic and simple tool for code auto completion, built upon GPT-2",natural-language-processing
1546,2020-06-22 00:46:32,DeepSNAP,Python library assists deep learning on graphs.,other
1547,2020-06-22 00:50:30,RoBERTa meets TPUs,Understanding and applying the RoBERTa model to the current challenge.,natural-language-processing
1549,2020-06-22 01:00:45,Deep Model-Based RL for Real-World Robotic Control,Short talk about model-based RL by Sergey Levine.,other
1551,2020-06-22 03:17:48,Pokemon Classifier,I want to build a classifier that can classify 150 types of Pokemon.,computer-vision
1552,2020-06-22 03:45:01,Workshop on Scalability in Autonomous Driving - Andrej Karpathy,An overview of autonomous driving and computer vision at Tesla.,computer-vision
1560,2020-06-22 15:56:00,Battle-Tested Techniques for Scoping Machine Learning Projects,One of the challenges of managing an ML project is project scoping. Even small changes in data or architecture can create huge differences in model outputs.,mlops
1563,2020-06-22 16:04:10,Classify photos in 600 classes using nine million Open Images,"If you’re looking build an image classifier but need training data, look no further than Google Open Images.",computer-vision
1569,2020-06-22 16:52:01,Trackable,The project deals with tracking humans in a narrow hallway under different lighting conditions.,computer-vision
1571,2020-06-23 02:04:12,Stochastic Segmentation Networks,An efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture.,computer-vision
1575,2020-06-23 02:30:20,Deep Learning for Computer Vision ,Special topics class on deep learning for computer vision from the University of Michigan taught by Justin Johnson.,computer-vision
1576,2020-06-23 02:37:15,VPSNet for Video Panoptic Segmentation,Video panoptic segmentation by generating consistent panoptic segmentation as well as an association of instance ids across video frames.,computer-vision
1580,2020-06-24 03:00:16,What I Learned From Looking at 200 Machine Learning Tools,"To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find.",mlops
1581,2020-06-24 03:04:31,Discovering Symbolic Models from Deep Learning w/ Inductive Bias,A general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases.,other
1585,2020-06-24 03:18:20,Breaking the cycle—Colleagues are all you need,A novel approach to performing image-to-image translation between unpaired domains.,computer-vision
1587,2020-06-24 03:25:25,Deep Learning Based Text Classification: A Comprehensive Review,An overview of deep learning approaches to text classification.,natural-language-processing
1589,2020-06-24 03:33:09,jiant,A software toolkit for research on general-purpose text understanding models.,natural-language-processing
1592,2020-06-24 04:27:58,Text Classification,"Re-implemented an article (link is given below) which was on Text classification with CNN, beside this I tried out some ML classification algorithm.",natural-language-processing
1595,2020-06-24 15:42:20,multi-task-NLP,A utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.,natural-language-processing
1597,2020-06-25 00:17:39,Maximizing Business Impact with Machine Learning,how to effectively leverage machine learning to build intelligent products as efficiently as possible.,mlops
1598,2020-06-25 00:29:18,Automatic Data Augmentation for Generalization in Deep RL,We compare three approaches for automatically finding an appropriate augmentation combined with two novel regularization terms for the policy and value function,other
1599,2020-06-25 00:42:36,High-Fidelity Generative Image Compression,How to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system.,computer-vision
1602,2020-06-25 04:03:38,Unet Model for Image Segmentation With EfficientNet Encoder,Implemented using tensorflow 2.2.0 with custom train and test step.,computer-vision
1603,2020-06-25 10:40:56,A Million of ML Predictions at the Tip of Your Fingers,Announcement - SashiDo is breaking the barrier to Machine Learning by introducing a fully open-sourced Content Moderation Service.,computer-vision
1605,2020-06-26 02:19:39,NetHack Learning Environment (NLE),A procedurally-generated grid-world dungeon-crawl game that strikes a great balance between complexity and speed for single-agent RL research.,other
1606,2020-06-26 02:24:53,Paraphrase Generation Using T5 model,Simple application using T5 base model fine tuned in Quora Question Pairs to generate paraphrased questions.,natural-language-processing
1607,2020-06-26 02:28:15,Message Passing Query Embedding,"MPQE is a model for answering complex queries over knowledge graphs, that learns embeddings of entities in the knowledge graph, & embeddings for variable types.",other
1608,2020-06-26 02:31:17,Quantifying Attention Flow in Transformers,"I explain two simple but effective methods, called Attention Rollout and Attention Flow",natural-language-processing
1614,2020-06-27 04:15:51,Natural Language Processing Roadmap,Roadmap for learning NLP topics.,natural-language-processing
1615,2020-06-27 04:29:04,Weight Poisoning Attacks on Pre-trained Models,"How Bert can be infused with nefarious behavior, even after fine-tuning.",natural-language-processing
1616,2020-06-27 04:37:16,Leveraging Temporal Context for Object Detection,"Object detection architecture leveraging contextual clues across time for each camera deployment in a network, improving recognition of objects",computer-vision
1617,2020-06-27 04:42:47,Expressive Power of Graph Neural Networks,"Graph isomorphism problem, the Weisfeiler-Lehman heuristic for graph isomorphism testing, and how it can be used to analyse the expressive power of GNNs.",other
1620,2020-06-27 10:27:43,rlx: A modular Deep RL library for research,"""rlx"" is a Deep RL library written on top of PyTorch & built for educational and research purpose.",other
1622,2020-06-27 14:18:13,Building AI Trading Systems,Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.,other
1623,2020-06-27 14:20:49,Introduction to NLP using Fastai,Implementing and decoding the revolutionary ULMFiT approach to train a language model on any downstream NLP task.,natural-language-processing
1629,2020-06-28 07:37:00,TF Lite Semantic Segmentation Models,Faster and lighter TF Lite models can perform semantic segmentation. ,computer-vision
1630,2020-06-28 07:40:40,Semantic Segmentation + Background Removal + Style Transfer,"Running multiple TF Lite models to perform semantic segmentation, remove background, and apply style transfer. ",computer-vision
1636,2020-06-29 00:00:47,Automatic translation of the SQUAD dataset to spanish,"Machine translation is used on the SQuAD dataset to produce an equivalent dataset in Spanish. Word alignment is applied to produce a synthetic spanisQA corpus.
",natural-language-processing
1638,2020-06-29 02:56:43,Dakshina Dataset,A collection of text in both Latin and native scripts for 12 South Asian languages.,natural-language-processing
1639,2020-06-29 02:58:52,Computer Vision Recipes,This repository provides examples and best practice guidelines for building computer vision systems.,computer-vision
1644,2020-06-29 12:42:44,A research guide for data scientists,Tips on research from top data scientists,natural-language-processing
1645,2020-06-29 17:16:17,Using Data Science Pipelines for Disaster Response,Uses ETL and ML pipeline to build an NLP system for classification of messages into appropriate disaster categories,natural-language-processing
1646,2020-06-29 19:47:58,Twitter Turing Test,Can you guess whether this tweet is written by a human or generated by a neural network?,natural-language-processing
1648,2020-06-30 02:34:54,STUMPY: A Powerful and Scalable Python Library for Time Series,"STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks.",other
1649,2020-06-30 02:39:32,Model Serving using FastAPI and streamlit,Simple example of usage of streamlit and FastAPI for ML model serving.,computer-vision
1650,2020-06-30 02:49:57,The Reformer - Pushing the Limits of Language Modeling,An in-depth understanding of each of the key features of the Reformer.,natural-language-processing
1651,2020-06-30 02:52:41,High-Resolution Image Inpainting,"High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling.
",computer-vision
1653,2020-06-30 03:01:50,MARGE: Pre-training via Paraphrasing,"A retrieval model maps a document to a set of related documents, which a reconstruction model paraphrases to maximize the likelihood of the original. ",natural-language-processing
1657,2020-06-30 18:00:11,Fast Api with Dockerization of your ML Models,In this GitHub repo you can able to know and learn how to build a fast API for testing your ML model and can test your ML model with UI and to Dockerize your ML,mlops
1658,2020-07-01 02:22:10,SimCLR - Contrastive Learning of Visual Representations,How to load pretrained/finetuned SimCLR models from hub modules for fine-tuning.,computer-vision
1662,2020-07-01 07:00:50,Image synthesis at CVPR 2020,An overview of the different approaches to image synthesis at CVPR 2020.,computer-vision
1663,2020-07-01 07:08:45,Sktime,A python toolbox for machine learning with time series.,other
1664,2020-07-01 07:14:00,"Sentiment Analysis: Key Milestones, Challenges and New Directions","An overview of sentiment analysis, it's progress and what's ahead.",natural-language-processing
1666,2020-07-01 07:20:52,Serverless BERT with HuggingFace and AWS Lambda,"Build a serverless question-answering API with BERT, HuggingFace, the Serverless Framework, and AWS Lambda.",natural-language-processing
1668,2020-07-01 13:33:49,Model-based Reinforcement Learning: A Survey,"A survey of the integration of both fields, better known as model-based reinforcement learning.",other
1677,2020-07-02 04:06:19,Building Level 3 Conversational AI Assistants,"Presentations, panels, and fireside chats addressing all topics related to the creation of Level 3 AI assistants.",natural-language-processing
1678,2020-07-02 12:13:19,NSFW Image Classification REST API built with TensorFlow.JS,A ready-to-use & open-source NSFW Image Classification REST API built with TensorFlow.JS and NSFW.JS for effortless Content Moderation,computer-vision
1688,2020-07-03 04:23:58,Python Implementation of Reinforcement Learning: An Introduction ,"Plot replications, exercise solutions and Anki flashcards for the entire book by chapters.",other
1691,2020-07-03 04:40:05,The Simplest Way to Serve your NLP Model in Production w/ Python ,"From scikit-learn to Hugging Face Pipelines, learn the simplest way to deploy ML models using Ray Serve.",mlops
1698,2020-07-04 01:07:48,Learning to Cartoonize Using White-box Cartoon Representations,An approach for image cartoonization using GANs.,computer-vision
1699,2020-07-04 01:10:18,Reinforcement Learning Tutorial,"Important reinforcement learning (RL) algorithms, including policy iteration, Q-Learning, and Neural Fitted Q.",other
1702,2020-07-04 04:51:18,Face Recognition Techniques,Face Detection and Recognition techniques using traditional CV and also using new deep learning method.,computer-vision
1704,2020-07-04 10:42:53,LSTM Forecast Model for Stock Price Prediction using Keras," Easy to understand LSTM forecast model for Stock Price Prediction. The dataset contains daywise details of the GOOGL stock from May,2019-May 2018.",other
1706,2020-07-04 11:05:28,PokeZoo,A deep learning based web-app developed using the MERN stack and Tensorflow.js. ,computer-vision
1710,2020-07-05 05:47:35,NLP-task-visualizer-app,This application designed with streamlit library will help in visualizing NLP tasks on text entered by you. ,natural-language-processing
1721,2020-07-07 04:21:20,TensorflowTTS,Real-Time State-of-the-art Speech Synthesis for Tensorflow 2.,natural-language-processing
1722,2020-07-07 04:23:38,spaczz: Fuzzy matching and more for spaCy,Fuzzy matching and more functionality for spaCy.,natural-language-processing
1723,2020-07-07 04:26:45,BioSyn,Biomedical Entity Representations with Synonym Marginalization,natural-language-processing
1724,2020-07-08 04:02:50,Image Classifier: In the Browser,Using Tensorflow.js to make the prediction directly in the browser.,computer-vision
1726,2020-07-08 04:15:07,Photon: A Robust Cross-Domain Text-to-SQL System,"A robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined. ",natural-language-processing
1728,2020-07-08 04:24:07,Bounding Box Prediction from Scratch using PyTorch,Multi-Task learning — Bounding Box Regression + Image Classification,computer-vision
1729,2020-07-08 04:28:13,Comment Classification Using BERT (multi-language) Fine-Tuning,We are going to use BERT layer in a model applying Keras.,natural-language-processing
1730,2020-07-08 04:30:28,TextBrewer,a PyTorch-based model distillation toolkit for natural language processing.,natural-language-processing
1737,2020-07-08 18:22:40,codeBERT - Automated code docstring review with transformers,"codeBERT provide a one command line to check if your code docstrings are up-to-date.
",natural-language-processing
1748,2020-07-09 02:23:25,Continuous Machine Learning (CML),CML helps to organize MLOps infrastructure on top of the traditional software engineering stack instead of creating separate AI platforms.,mlops
1750,2020-07-09 10:30:30,picTranslate: Seamless live Image Text translator,"Given an image with text on it, this app can give you a new image with text modified into a different language.",computer-vision
1753,2020-07-10 02:44:11,TUDatasets,A collection of benchmark datasets for graph classification and regression.,other
1754,2020-07-10 02:46:07,Full Stack Deep Learning,Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.,mlops
1755,2020-07-10 02:51:24,Easy OCR,"Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai.
",computer-vision
1759,2020-07-10 18:54:54,Emotion Recognition from Tom and Jerry videos,"Developed an application that classifies the emotion depicted by Tom and Jerry in each frame into one of the following : happy, angry, sad or suprised.",computer-vision
1767,2020-07-11 05:05:31,Imagenette,Imagenette is a subset of 10 easily classified classes from Imagenet.,computer-vision
1768,2020-07-11 05:08:02,TextAugment,Improving Short Text Classification through Global Augmentation Methods,natural-language-processing
1769,2020-07-11 05:10:10,niacin,"A Python library for replacing the missing variation in your text data.
",natural-language-processing
1771,2020-07-11 05:16:17,Albumentations,Fast image augmentation library and easy to use wrapper around other libraries.,computer-vision
1772,2020-07-11 05:19:05,Augmentor,Image augmentation library in Python for machine learning.,computer-vision
1777,2020-07-11 05:37:12,tsfresh,Automatic extraction of relevant features from time series.,other
1792,2020-07-11 06:28:58,Anomaly Detection Toolkit (ADTK),"A Python toolkit for rule-based/unsupervised anomaly detection in time series
",other
1795,2020-07-11 06:37:35,Chakin ,Simple downloader for pre-trained word vectors.,natural-language-processing
1796,2020-07-11 06:39:39,Top2Vec,"Top2Vec learns jointly embedded topic, document and word vectors.
",natural-language-processing
1797,2020-07-11 06:42:29,Contextualized Topic Models,A python package to run contextualized topic modeling.,natural-language-processing
1800,2020-07-11 06:51:58,jellyfish,🎐 a python library for doing approximate and phonetic matching of strings.,natural-language-processing
1802,2020-07-11 06:57:28,SentencePiece,"Unsupervised text tokenizer for Neural Network-based text generation.
",natural-language-processing
1803,2020-07-11 06:59:08,A Deep Dive into the Wonderful World of Preprocessing in NLP,A glimpse into the surprisingly deep and interesting world of preprocessing in NLP.,natural-language-processing
1813,2020-07-11 07:45:01,Pytest,"The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
",mlops
1817,2020-07-11 07:55:23,Artifacts - Weights & Biases,"Effortless pipeline tracking and production model management
",mlops
1818,2020-07-11 08:07:35,DeepkitAI,The Open-Source Machine Learning Devtool and Training Suite.,mlops
1819,2020-07-11 08:14:03,Neptune.ai,The most lightweight experiment management tool that fits any workflow.,mlops
1820,2020-07-11 08:17:17,Rasa,An open source machine learning framework to automate text-and voice-based conversations. ,natural-language-processing
1831,2020-07-11 11:36:26,TF Sprinkles,Fast and efficient sprinkles augmentation implemented in TensorFlow.,computer-vision
1834,2020-07-11 17:19:43,Laplacian Pyramid Reconstruction and Refinement for Semantic Seg., Pytorch implementation of multi-resolution reconstruction architecture based on a Laplacian pyramid that uses skip connections.,computer-vision
1836,2020-07-11 18:15:19,Training a pets detector model with TFOD API (TF 2),"In this notebook, we will be training a custom object detection model using the latest TensorFlow Object Detection (TFOD) API which is based on TensorFlow 2.2. ",computer-vision
1840,2020-07-12 00:59:27,TensorFlow 2 meets the Object Detection API,TF Object Detection API (OD API) officially supports TensorFlow 2!,computer-vision
1843,2020-07-12 13:35:20,Cortex,Build machine learning APIs.,mlops
1844,2020-07-12 16:24:10,Semi-Supervised Learning in Computer Vision,A comprehensive overview of recent semi-supervised learning methods in Computer Vision,computer-vision
1845,2020-07-12 21:42:52,Face Predicting Web App,Interactive Deep Learning Model that utilizes your computer webcam to predict your age and gender in seconds! ,computer-vision
1847,2020-07-13 03:46:32,Driver Identification Based on Vehicle's telematics data,"In this paper, we proposed a deep learning model, which can identify drivers from their driving behaviors based on vehicle telematics data.",computer-vision
1848,2020-07-13 05:00:40,Comprehensive analysis of important metrics in ML,"In this work, the authors present a comprehensive analysis of important metrics in practical applications.",computer-vision
1851,2020-07-13 15:21:13,StreamAlert,"A serverless, realtime data analysis framework which empowers you to ingest, analyze, and alert on data from any environment, using datasources and alerts.",mlops
1855,2020-07-14 03:17:25,ULMFiT Airline Sentiment Analysis,Transfer Learning using pretrained ULMFiT model,natural-language-processing
1856,2020-07-14 03:21:00,DeepDream Video Style Transfer,DeepDream on Video,computer-vision
1859,2020-07-14 04:01:18,"You Trained a Machine Learning Model, Now What?","Three often overlooked parts of this process occur after the model is actually built: model evaluation, deployment, and monitoring.",mlops
1860,2020-07-14 09:53:19,NSFW Image Moderation Automation Engine built with TensorFlow.JS ,"An open-source NSFW Image Classifier including an Automation Engine for fast deletion & moderation built with Node.js, TensorFlow, and Parse Server",computer-vision
1865,2020-07-14 22:55:08,PDFTableExtract,Build a parser to extract the table in PDF document with RetinaNet,computer-vision
1867,2020-07-14 23:03:02,YOLOv4 With TensorFlow,"YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite.",computer-vision
1868,2020-07-15 03:52:31,Selfie2Anime with TFLite,An end-to-end tutorial with TensorFlow Lite for Selfie2Anime (U-GAT-IT). ,computer-vision
1869,2020-07-15 20:31:37,Bridging PyTorch and TVM,"Taking Hugging Face transformer BERT from PyTorch and running it on
ApacheTVM for both inference (with reasonable timings) and training.",natural-language-processing
1871,2020-07-16 03:58:21,Summarize a webapge,A Flask application that extracts and summarizes webpage using Natural Language Processing. Powered by nlp-akash.,natural-language-processing
1872,2020-07-16 04:19:37,An Icon Classifier with TensorFlow Lite Model Maker,An Icon Classifier with TensorFlow Lite Model Maker,computer-vision
1879,2020-07-16 17:40:33,Cross-lingual Transfer Learning - Sebastian Ruder,"An overview of approaches that transfer knowledge across languages and enable us to scale NLP models to more of the world's 7,000 languages.",natural-language-processing
1880,2020-07-16 17:43:48,AdapterHub: A Framework for Adapting Transformers,Huggingface Transformers + Adapters,natural-language-processing
1882,2020-07-16 17:51:48,Object Detection with RetinaNet,Implementing RetinaNet: Focal Loss for Dense Object Detection.,computer-vision
1884,2020-07-17 01:41:33,Deploying your ML Model with TorchServe,"In this talk, Brad Heintz walks through how to use TorchServe to deploy trained models at scale without writing custom code. ",mlops
1886,2020-07-17 08:27:56,Medical Zoo - 3D Multi-modal Medical Image Segmentation,My articles on deep learning in medical imaging,computer-vision
1887,2020-07-17 16:48:13,Computer Vision Pretrained Models,A collection of computer vision pre-trained models.,computer-vision
1889,2020-07-17 17:20:20,NLP Pretrained Models,"A collection of Natural language processing pre-trained models.
",natural-language-processing
1896,2020-07-19 00:40:37,Machine Learning Production Pipeline,"Project Flow and Landscape
",mlops
1898,2020-07-19 00:47:53,Tempering Expectations for GPT-3 and OpenAI’s API,"A closer look at the ""magic"" behind GPT-3 and caveats to be aware of.",natural-language-processing
1899,2020-07-19 03:59:41,StyleGAN Encoder,Encodes real images into the latent space of a StyleGAN model.,computer-vision
1900,2020-07-19 04:12:40,WikiArt StyleGAN 2 Model,A conditional StyleGAN 2 model trained on images from WikiArt,computer-vision
1902,2020-07-19 10:19:24,Indian Paper Currency Prediction,"The trained model takes an image (Indian Paper Currency) as an input and predict the class of image from 10, 20, 50, 100, 200, 500, 2000 denomination.",computer-vision
1903,2020-07-19 11:31:25,"Neural Style Transfer (Gatys et al., PyTorch)",My implementation of the original neural style transfer paper by Gatys et al. (In PyTorch).,computer-vision
1904,2020-07-19 12:44:53,Implementation of Face Net in TensorFlow - 2.0,This repository is a naive unofficial implementation of Face Net paper - 2015 .This implementation opts online mode of semi - hard triplet mining.,computer-vision
1910,2020-07-19 15:44:21,Azure Machine Learning Template,Azure Machine Learning template for MNIST classifier,mlops
1913,2020-07-19 16:55:33,Teachable Machine (Image Classifier),A teachable image classifier that runs on any browser built using TensorFlow JS.,computer-vision
1914,2020-07-19 16:59:37,TensorFlow JS- Object Detection in Browser,A real-time object detection model in your browser using TensorFlow JS.,computer-vision
1916,2020-07-20 00:01:38,How to Stop Worrying About Compositionality,"Review the tenets of compositionality, and to highlight how each theory has evolved to match particular theoretical positions about the nature of language.",natural-language-processing
1918,2020-07-20 05:48:38,Spacy-Go,spacy-go is Golang interface for accessing linguistic annotations provided by spaCy using Google's gRPC. This module only supports basic functionalities like lo,natural-language-processing
1919,2020-07-20 05:53:12,Dframcy,DframCy is a light-weight utility module to integrate Pandas Dataframe to spaCy's linguistic annotation and training tasks.,natural-language-processing
1921,2020-07-20 14:04:48,NSFW Image Moderation Admin App with ReactJS,"A fully-functional NSFW Admin Application for simplified image classification & moderation built with Node.js, TensorFlow.js, and React",computer-vision
1923,2020-07-20 18:59:04,PyTorch Geometric Temporal,A Temporal Extension Library for PyTorch Geometric ,other
1924,2020-07-20 20:34:47,Why is it Important to Monitor Machine Learning Models?,The importance of monitoring and how monitoring ML is different from application performance management (APM).,mlops
1925,2020-07-20 20:54:00,PyTorch Implementation of PaletteNet,"PyTorch implementation of PaletteNet: Image Recolorization with Given Color Palette (Cho et al., 2017).",computer-vision
1927,2020-07-20 21:21:12,ECG arrhythmia classification using a convolutional neural net,This is an implementation of the paper on ECG arrhythmia classification https://arxiv.org/pdf/1804.06812.pdf.,computer-vision
1929,2020-07-20 23:55:33,Structured Self Attention,Implementation for the paper A Structured Self-Attentive Sentence Embedding (https://arxiv.org/abs/1703.03130 ). Model interpretability / explainability.,natural-language-processing
1933,2020-07-21 01:42:42,TurboTransformers,A fast and user-friendly runtime for transformer inference on CPU and GPU.,natural-language-processing
1938,2020-07-21 11:50:53,Rasa NLU Examples,Experimental components for Rasa NLU pipelines. ,natural-language-processing
1940,2020-07-21 19:01:54,Change Detection using Siamese networks,"The blog is a primer on Siamese Networks and how they're used for observing change in satellite images over time, or observing facial changes as people age",computer-vision
1941,2020-07-21 19:13:05,My Artificial Intelligence Bookmarks,"A curated list of my reads, implementations, and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world.",natural-language-processing
1943,2020-07-22 03:32:30,Do we Need Deep Graph Neural Networks?,Does depth in graph neural network architectures bring any advantage?,other
1945,2020-07-22 03:39:13,Pandera,A flexible and expressive pandas data validation library.,mlops
1952,2020-07-24 06:28:15,TensorFlow Serving,"A flexible, high-performance serving system for machine learning models, designed for production environments. ",mlops
1953,2020-07-24 06:30:44,BentoML,BentoML is an open-source framework for high-performance ML model serving.,mlops
1954,2020-07-24 06:43:59,Azure ML,MLOps using Azure ML.,mlops
1955,2020-07-24 06:47:29,Shape and Viewpoint without Keypoints,"Recover the 3D shape, pose and texture from a single image, trained on an image collection without any ground truth 3D shape, multi-view, camera viewpoints.",computer-vision
1965,2020-07-25 02:58:40,model-logger,Model-Logger is a Python library for storing model's profile and rapid inter model comparison.,mlops
1968,2020-07-26 04:48:40,Sentiment Analysis With Transformers,"Sentiment analysis neural network trained by fine-tuning BERT, ALBERT, or DistilBERT on the Stanford Sentiment Treebank.",natural-language-processing
1971,2020-07-27 02:30:42,Attention based YOLO: Object Detection,"An easy to follow, YOLO implementation with keras lib. Used a attention based architecture to extract more fine grained information about object.",computer-vision
1977,2020-07-27 14:14:10,LabelDetection: simplifying the use and construction of deep dete,LabelDetection is a graphical tool that aims to facilitate all the steps required in the pipeline to construct and use a deep-learning base object detection mod,computer-vision
1978,2020-07-27 14:34:12,How to Set Up a Python Project For Automation and Collaboration,"How to set up a Python repo with unit tests, code coverage, lint checking, type checking, Makefile wrapper, and automated build with GitHub Actions.",mlops
1980,2020-07-27 14:51:03,Understanding & Implementing SimCLR - an ELI5 guide,"I explain the SimCLR and its contrastive loss function step by step, build image embeddings and then show how to use them to train image classifier on top.",computer-vision
1983,2020-07-28 04:14:12,CoreML Model Zoo,Collection of unified and converted pre-trained models.,computer-vision
1984,2020-07-28 04:18:00,How GPT3 Works - Visualizations and Animations,A compilation of my threads explaining GPT3. ,natural-language-processing
1985,2020-07-28 04:19:58,Temporal Graph Networks,"In this post, we describe Temporal Graph Network, a generic framework for deep learning on dynamic graphs.",other
1986,2020-07-28 07:44:13,Behavioral Testing of NLP models with CheckList,An overview of the “CheckList” framework for fine-grained evaluation of NLP models,natural-language-processing
1992,2020-07-29 03:41:04,Time series forecasting,A thorough introduction to time series forecasting using TensorFlow.,other
1993,2020-07-29 04:47:55,Real-time text detection with EAST in TFLite,Demonstrates the conversion process from the original EAST model to TFLite and how to use it on static images and also on real-time video feeds. ,computer-vision
1994,2020-07-29 04:51:30,Understanding the Effectivity of Ensembles in Deep Learning,"The report explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective by Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan.",computer-vision
1999,2020-07-30 03:57:32,Small differences in BLEU are meaningless,Only big differences in metric scores are meaningful in MT.,natural-language-processing
2002,2020-07-30 04:08:46,Multi-target in Albumentations,"Many images, many masks, bounding boxes, and key points. How to transform them in sync?",computer-vision
2005,2020-07-30 11:19:02,Social Distance Detection,"If people are very close to each other, a red bounding box is displayed around them indicating that they are not maintaining social distance.",computer-vision
2006,2020-07-30 11:30:56,Deep Learning Techniques for NLP in Healthcare,A talk discussing the recent advancements of deep learning to facilitate the adaption of NLP in the healthcare domain.,natural-language-processing
2008,2020-07-30 14:50:30,Extension to block NSFW content using AI,"NSFW Filter is an extension that blocks NSFW content from your browser.
It uses a computer vision model to detect NSFW content and hides it from the user.",computer-vision
2009,2020-07-30 14:55:57,ATLASS: AutoML using Transfer and Semi-Supervised Learning,"This repository includes the code, application, and notebooks for the work ""AutoML using Transfer and Semi-Supervised Learning"". The tools presented here can be",computer-vision
2012,2020-07-30 15:04:28,LabelStoma: stomata detection using YOLO,LabelStoma is a graphical image tool for automatically detecting stomata in images. ,computer-vision
2013,2020-07-30 15:07:54,DeepClas4Bio,DeepClas4Bio is a project that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks.,computer-vision
2016,2020-07-31 15:30:38,Meme Classifier Using TFlite and flutter,Meme classifier using fine tuned mobilenet. This app showcases how you can perform low latency realtime classification apps using TFlite,computer-vision
2020,2020-08-01 12:14:26,Text Summarization using TF-IDF Algorithm,This Article explains the TF-IDF algorithm and shows the implemtnation from scratch to summarize the text.,natural-language-processing
2022,2020-08-01 14:41:37,Simple Transformers,"Transformers for Classification, NER, QA, Language Modeling, Language Generation, T5, Multi-Modal, and Conversational AI.",natural-language-processing
2024,2020-08-01 14:49:31,DeText: A Deep Neural Text Understanding Framework,DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks.,natural-language-processing
2026,2020-08-01 15:04:37,Efficient Serverless Deployment of PyTorch Models on Azure,A tutorial for serving models cost-effectively at scale using Azure Functions and ONNX Runtime.,mlops
2027,2020-08-01 15:27:29,Nearest Celebrity Face,Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours. The input face,computer-vision
2030,2020-08-02 12:38:08,A Few Favorite Recipes in Computer Vision & Deep Learning,This blog post enlists a few of my favorite recipes in deep learning in the context of computer vision (as of August 2020).,computer-vision
2031,2020-08-02 14:46:10,NeuralQA - API and Visual Interface for Extractive QA,A Usable Library for Question Answering on Large Datasets with BERT,natural-language-processing
2032,2020-08-02 20:00:23,Object tracking in 75 lines of code,"Object tracking is straightforward conceptually. And if you have a good detector, simple methods can be pretty effective.",computer-vision
2033,2020-08-03 03:49:22,FARM: Framework for Adapting Representation Models,🏡 Fast & easy transfer learning for NLP. Harvesting language models for the industry.,natural-language-processing
2035,2020-08-04 02:49:24,Act - GitHub Actions locally,Run your GitHub Actions locally.,mlops
2038,2020-08-04 03:53:36,Curated papers & articles on DS & ML in production,"Learn how organizations & business solved machine learning problems, including problem statement, research, methodology, and results.",mlops
2039,2020-08-04 16:45:09,Tensorflow2 Object Detection Tutorial,"In this tutorial, we will be going step by step the complete training process of Tensorflow2 Object Detection. ",computer-vision
2042,2020-08-05 02:07:24,ONNX T5,"Summarization, translation, Q&A, text generation and more at blazing speed using a T5 version implemented in ONNX.",natural-language-processing
2043,2020-08-05 02:17:10,DeLighT: Very Deep and Light-weight Transformers,Similar or better performance than transformer-based models with significantly fewer parameters,natural-language-processing
2045,2020-08-05 06:40:32,Evaluation Metrics For Information Retrieval,Learn about common metrics used to evaluate performance of information retrieval systems,natural-language-processing
2047,2020-08-05 15:18:46,Test-Time Data Augmentation,Tutorial on how to properly implement test-time image data augmentation in a production environment with limited computational resources.,mlops
2048,2020-08-05 16:50:22,SadedeGel: An extraction based Turkish news summarizer,"""Sadede Gel"" in Turkish, means ""cut to the chase"". ",natural-language-processing
2051,2020-08-05 20:13:51,MobyDick word frequency,Getting the count of the words in Moby Dick story using both web scraping and NLP,natural-language-processing
2053,2020-08-05 20:30:33,Image Classification with Keras,Build a pipeline to train an image classifier in Keras and tune hyperparameters to optimize the performance of our classifier.,computer-vision
2054,2020-08-05 20:34:09,Dropout in PyTorch – An Example,"An example of adding Dropout to a PyTorch model, and observe the effect dropout has on the model's performance by tracking our models in Weights & Biases.",computer-vision
2057,2020-08-06 04:06:11,"Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes","An end-to-end example of deploying a machine learning product using Jupyter, Papermill, Tekton, GitOps and Kubeflow.",mlops
2061,2020-08-06 04:59:21,Detectron 2 Demo from Facebook,This Project contains the process of getting started with Facebook FAIR's detectron2 project on windows 10 without any Nvidia GPU.,computer-vision
2062,2020-08-06 12:38:55,Predict Vehicle Speed From Dash Cam Video,A series of experiments attempting to predict vehicle speed from dash cam videos using optical flow and neural networks.,computer-vision
2098,2020-08-06 23:15:45,Digital Image Processing in Python,Play around with pixel values with Python programming language.,computer-vision
2100,2020-08-07 04:24:28,A 2020 guide to Semantic Segmentation,"Concept of image segmentation, discuss the relevant use-cases, different neural network architectures involved in achieving the results, metrics and datasets.",computer-vision
2106,2020-08-08 15:06:18,Fast NST for Videos (+ person segmentation) 🎥 + ⚡💻 + 🎨 = ❤️,Create NST videos and pick separate styles for the person in the video and for the background.,computer-vision
2109,2020-08-09 07:24:57,Live demo : State-of-the-art MCQ Generator from any content,"Demo for state-of-the-art MCQ (Multiple Choice Questions) generator from any content built using T5 transformer, HuggingFace, and Sense2vec
",natural-language-processing
2111,2020-08-10 03:26:16,InvoiceNet,"Deep neural network to extract intelligent information from PDF invoice documents.
",computer-vision
2112,2020-08-10 03:41:31,Search for visual datasets,"By task, application, class, label or format.",computer-vision
2113,2020-08-10 04:01:03,GAN-BERT,Enhancing the BERT training with Semi-supervised Generative Adversarial Networks.,natural-language-processing
2114,2020-08-10 04:03:51,tsaug,A Python package for time series augmentation.,other
2116,2020-08-10 04:15:38,Machine Learning Pipelines for Kubeflow.,Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.,mlops
2117,2020-08-10 04:17:57,Structuring Unit Tests in Python,"Where to put tests, how to write fixtures and the awesomeness of test parametrization.",mlops
2121,2020-08-10 21:59:41,DeepR — Training TensorFlow Models for Production,DeepR is a Python library to build complex pipelines as easily as possible on top of Tensorflow.,mlops
2124,2020-08-11 00:20:42,Neural Architecture Search,"A look at neural architecture search w.r.t search space, search algorithms and evolution strategies.",other
2135,2020-08-13 01:52:06,Temporal Convolutional Networks for Time-Series,"We introduce several novels using TCN, including improving traffic prediction, sound event localization & detection, and probabilistic forecasting.",other
2136,2020-08-13 02:05:11,Machine Learning Deployment: Shadow Mode,"“How do I test my new model in production?” One answer, and a method I often employ when initially deploying models, is shadow mode.",mlops
2138,2020-08-13 18:12:46,Extract Stock Sentiment from News Headlines," In this project, you will generate investing insight by applying sentiment analysis on financial news headlines from Finviz. ",natural-language-processing
2141,2020-08-14 03:15:38,hloc - the hierarchical localization toolbox,Visual localization made easy.,computer-vision
2147,2020-08-15 01:17:07,Practical Tips and Tricks for Successful Transfer Learning,Training models to learn knowledge and skills from other related tasks that will transfer and boost performance on tasks of interest.,natural-language-processing
2148,2020-08-15 01:22:01,txtai: AI-powered search engine,AI-powered search engine.,natural-language-processing
2151,2020-08-15 05:32:22,Drowsiness Detection System using OpenCV and Flask in Python ,"This system provides an overview of a system that detects whether a person is drowsy while driving and if so, alerts him by using voice messages in real-time. ",computer-vision
2155,2020-08-15 14:49:16,"GPT-3, The model simply knows!",Brief Introduction about the gigantic GPT-3. a new leap in AI and Natural Language processing. ,natural-language-processing
2159,2020-08-16 01:02:18,Solaris,CosmiQ Works Geospatial Machine Learning Analysis Toolkit.,computer-vision
2163,2020-08-17 03:19:46,Safe Space - Github Action,Github action that checks the toxicity level of comments and PR reviews to help make repos safe spaces.,natural-language-processing
2164,2020-08-17 03:24:46,Intro to Autoencoders,"This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.",computer-vision
2166,2020-08-17 05:19:41,Pix2Pix,"Tensorflow 2.0 Implementation of the paper Image-to-Image Translation using Conditional GANs by Philip Isola, Jun-Yan Zhu, Tinghui Zhou and Alexei A. Efros.",computer-vision
2167,2020-08-17 06:27:31,Insight,Project Insight is designed to create NLP as a service with code base for both front end GUI (streamlit) and backend server (FastAPI) the usage of transformers ,natural-language-processing
2168,2020-08-17 10:55:43,Onceupon.space,NLP experiment in story-telling that creates illustrations (text to sketch) and content (text generation),natural-language-processing
2173,2020-08-18 04:16:33,Fine-tuning with custom datasets,This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets.,natural-language-processing
2185,2020-08-18 23:12:27,Language Interpretability Tool (LIT),"The Language Interpretability Tool (LIT) is a visual, interactive model-understanding tool for NLP models.",natural-language-processing
2188,2020-08-19 15:16:46,Great Expectations,Always know what to expect from your data.,mlops
2193,2020-08-20 00:39:05,Effective testing for machine learning systems,"Why testing machine learning systems can be different, and discuss some strategies for writing effective tests for machine learning systems.",mlops
2202,2020-08-22 03:55:27,Graph Representation Learning Book,"Introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.",other
2203,2020-08-22 05:58:20,Image Similarity Search in PyTorch,"Simple Convolutional Auto-encoder based image similarity
search to find similar images to given image or features.
Fully written in PyTorch.",computer-vision
2204,2020-08-22 17:19:00,Tensorflow Object Detection with Tensorflow 2,Object Detection with Tensorflow 2 and the Tensorflow Object Detection API ,computer-vision
2207,2020-08-23 04:38:45,Rules of Machine Learning: Best Practices for ML Engineering,A basic knowledge of machine learning get the benefit of best practices in machine learning from around Google.,mlops
2214,2020-08-24 11:16:47,vedaseg,vedaseg is an open source semantic segmentation toolbox based on PyTorch.,computer-vision
2215,2020-08-24 11:52:10,vedastr,vedastr is an open source scene text recognition toolbox based on PyTorch.,computer-vision
2218,2020-08-25 13:57:49,CascadeTabNet,"An approach for end-to-end table detection and structure recognition from image-based documents
",computer-vision
2220,2020-08-25 16:13:31,"Table Detection, Information Extraction and Structuring using ML",Table Extraction (TE) is the task of detecting and decomposing table information in a document.,natural-language-processing
2223,2020-08-26 04:21:37,AxCell,Automatic Extraction of Results from Machine Learning Papers,computer-vision
2226,2020-08-27 01:54:16,Hyperparameter Optimization for 🤗 Transformers: A Guide,"Basic grid search is not the most optimal, and in fact, the hyperparameters we choose can have a significant impact on our final model performance.",natural-language-processing
2235,2020-08-27 16:03:12,Shift-Ctrl-F: Semantic Search for the Browser,🔎: Search the information available on a webpage using natural language instead of an exact string match.,natural-language-processing
2238,2020-08-28 01:24:08,Spinning Up in Deep RL (OpenAI),An educational resource to help anyone learn deep reinforcement learning.,other
2239,2020-08-28 07:07:39,An Introduction to Adversarial Examples in Deep Learning,"This report provides an intuitive introduction to adversarial examples, discusses a wide variety of different adversarial attacks and, most notably, provides ad",computer-vision
2242,2020-08-29 08:10:21,Deep dive into ROI layer in Object Detection Models,In this blog post we will implement in torch ROI Pool and ROI Align models from scratch.,computer-vision
2245,2020-08-30 02:51:07,On the Bottleneck of Graph Neural Networks and its Implications,The mechanism of propagating information between neighbors creates a bottleneck when every node aggregates messages from its neighbors.,other
2247,2020-08-30 11:48:19,Unsupervised Keyphrase Extraction,Learn about unsupervised algorithms for automatically extracting representative keyword and phrases from documents,natural-language-processing
2251,2020-08-31 10:05:12,Practical AI: Using NLP word embeddings to solve localization ,"Using NLP word vectors (word2vec, glove, etc) in a novel way to solve the problem of localization in edtech.",natural-language-processing
2252,2020-08-31 23:40:26,Explore then Execute,Adapting without Rewards via Factorized Meta-Reinforcement Learning,other
2255,2020-09-01 04:49:38,"Tensorflow, Pytorch, Transformer, Fastai, etc. Tutorials","BERT Classification, Question Answering, Seq2Seq Machine Translation, Contextual Topic Modeling, Large Scale Multilabelclassification, etc",natural-language-processing
2258,2020-09-02 09:05:08,Graph Convolutions for dummies,An article explaining Graph Convolutional Networks as simply as possible.,other
2259,2020-09-02 23:08:03,ECCV 2020: Some Highlights,A sort of a snapshot of the conference by summarizing some papers (& listing some) that grabbed my attention.,computer-vision
2260,2020-09-02 23:13:20,CVPR 2020: A Snapshot,A snapshot of the conference by summarizing some papers (& listing some) that grabbed my attention.,computer-vision
2263,2020-09-03 23:05:32,TTT: Fine-tuning Transformers with TPUs or GPUs acceleration,"TTT is short for a package for fine-tuning 🤗 Transformers with TPUs, written in Tensorflow2.0+.",natural-language-processing
2264,2020-09-04 01:24:22,MushroomRL,Python library for Reinforcement Learning.,other
2267,2020-09-04 02:50:39,What Is MLOps?,"Machine learning operations, MLOps, are best practices for businesses to run AI successfully with help from an expanding software products and cloud services.",mlops
2268,2020-09-05 01:06:07,NLP Course | For You,This is an extension to the (ML for) Natural Language Processing course I teach at the Yandex School of Data Analysis (YSDA) since fall 2018. ,natural-language-processing
2269,2020-09-05 01:09:06,Learning to Summarize with Human Feedback,Human feedback models outperform much larger supervised models and reference summaries on TL;DR,natural-language-processing
2273,2020-09-05 18:22:44,ONNX Transformers,Accelerated NLP pipelines for fast inference 🚀 on CPU. Built with 🤗 Transformers and ONNX runtime.,natural-language-processing
2275,2020-09-06 07:26:21,hugdatafast: huggingface/nlp + fastai,The elegant integration of huggingface/nlp and fastai2 and handy transforms using pure huggingface/nlp ,natural-language-processing
2280,2020-09-06 18:59:46,Top 10 Deep Learning Breakthroughs — Deep Reinforcement Learning,The article unravels the journey behind reaching the point when Reinforcement Learning combined with Deep Learning defeated a Go player world champion.,other
2283,2020-09-07 07:13:04,Data analysis made easy: Text2Code for Jupyter notebook,A jupyter notebook extension for Text2Code for basic pandas and plotly commands,natural-language-processing
2284,2020-09-07 10:42:32,electra_pytorch: ELECTRA in PyTorch (fastai + huggingface),Unofficial reimplementation of <ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators>,natural-language-processing
2285,2020-09-07 13:36:55,Images of radio boxes,I have collected about 15+k raw images of radio boxes across 500+ forms and hand-picked 200+ images that can be used to determine if a radio box is checked.,computer-vision
2287,2020-09-07 20:56:51,omega|ml - building and deploying ML models the easy way,Deploying ML is hard. It should not be. omega|ml makes it a breeze.,mlops
2290,2020-09-09 00:16:32,Fine-tune a non-English GPT-2 Model with Huggingface," In this tutorial, we are going to use the transformers library by Huggingface. We will use the new Trainer class and fine-tune out GPT-2 model.",natural-language-processing
2294,2020-09-09 16:14:37,Getting started with large-scale ETL jobs using Dask and AWS EMR,"EMR is AWS’s distributed data platform, which we can interact with and submit jobs to from a JupyterLab notebook running on our local machine.",mlops
2295,2020-09-09 16:36:45,How to Create a Cartoonizer with TensorFlow Lite?,An end-to-end tutorial on how to convert to TensorFlow Lite (TFLite) model and deploy it to an Android app for cartoonizing an image captured by camera.,computer-vision
2296,2020-09-10 01:15:57,How to Test Machine Learning Code and Systems,"🚦 Minimal examples of testing machine learning for correct implementation, expected learned behaviour, and model performance.
",mlops
2298,2020-09-11 00:02:10,torchCDE,Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.,other
2299,2020-09-11 00:07:11,Latent graph neural networks: Manifold learning 2.0?,Parallels between recent works on latent graph learning and older techniques of manifold learning.,other
2300,2020-09-11 00:11:14,Real Python Recommendation Engine,A full stack data science project that performs document similarity on RealPython.com content. Content recommendations are implemented via a Chrome extension.,natural-language-processing
2304,2020-09-11 17:54:04,Graph Neural Networks,A descriptive guide for Graph Neural Networks.,other
2317,2020-09-14 05:32:45,End-to-end Object Detection in TensorFlow Lite,"This project shows how to train a custom detection model with the TFOD API, optimize it with TFLite, and perform inference with the optimized model.",computer-vision
2318,2020-09-14 11:55:33,Jepto - Digital Marketing Analytics,KPI Prediction and Anomaly Detection of digital marketing data for both technical and non-technical marketers and business owners.,other
2319,2020-09-14 19:21:33,Cartoonizer with TensorFlow.js,An app to turn your photos into cartoon-styled images 🎨 within your browsers using White-box Cartoonization GAN.,computer-vision
2325,2020-09-16 13:43:20,Implementing Content-Based Image Retrieval with Siamese Networks,"With content-based image retrieval, we refer to the task of finding images containing attributes which are not in the image metadata, but in its visual content.",computer-vision
2326,2020-09-17 00:18:51,NLP for Developers: Multilingual NLP | Rasa,"In this video, Rasa Developer Advocate Rachael will talk about common approaches to handle language input in more than one language.",natural-language-processing
2327,2020-09-17 15:36:45,Paint with Machine Learning,This web app allows you to create a landscape painting in the style of Bob Ross using a deep learning model served using a Spell model server.,computer-vision
2328,2020-09-17 16:04:29,Distilling Knowledge in Neural Networks,This project demonstrates the compelling model optimization technique - knowledge distillation with code walkthroughs in TensorFlow. ,computer-vision
2332,2020-09-18 08:49:55,Recurrent Neural Networks: building GRU cells VS LSTM cells ,What are the advantages of RNN’s over transformers? When to use GRU’s over LSTM? What are the equations of GRU really mean? How to build a GRU cell in Pytorch?,natural-language-processing
2341,2020-09-20 00:34:03,PyTorch Forecasting,Time series forecasting with PyTorch.,other
2342,2020-09-20 03:24:58,Norfair,Lightweight Python library for adding real-time 2D object tracking to any detector.,computer-vision
2344,2020-09-21 00:20:00,Labelai,"Labelai is an online tool designed to label images, useful for training AI models.",computer-vision
2345,2020-09-21 00:26:02,Remo,🐰 Python lib for remo - the app for annotations and images management in Computer Vision.,computer-vision
2348,2020-09-21 23:47:06,Layered Neural Rendering for Retiming People in Video,Manipulating and editing the time in which different motions of individuals in the video occur.,computer-vision
2351,2020-09-22 03:42:58,Simple Transformers: Transformers Made Easy,Simple Transformers removes complexity and lets you get down to what matters – model training and experimenting with the Transformer model architectures.,natural-language-processing
2353,2020-09-22 13:04:04,TF Geometric,Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x.,other
2356,2020-09-23 04:56:15,"Part 2: Deep Representations, a way towards neural style transfer",A top-down approach to conceiving neural style transfer,computer-vision
2357,2020-09-23 10:27:15,Sudoku Solver,Solving Sudoku by extracting the puzzle from photo using Computer Vision and OCR and solving it.,computer-vision
2360,2020-09-23 13:56:29,"3D Face: Fast, Accurate and Stable Reconstruction","This work extends the previous work 3DDFA, named 3DDFA_V2, titled Towards Fast, Accurate and Stable 3D Dense Face Alignment, accepted by ECCV 2020. ",computer-vision
2368,2020-09-25 07:47:27,TableQA,AI tool for querying natural language on tabular data like csvs and other dataframes.,natural-language-processing
2369,2020-09-25 15:44:08,GP-GAN: Towards Realistic High-Resolution Image Blending,Blending composite images using a generative model and a Gaussian-Poisson equation with a Laplacian Pyramid,computer-vision
2371,2020-09-25 18:10:13,From Research to Production with Deep Semi-Supervised Learning,Semi-Supervised Learning (SSL) has blossomed in the deep learning research community — we share lessons learned over 15 months of taking SSL into production.,mlops
2372,2020-09-25 18:39:59, A spaced repetition app for keeping your reinforcement learning,We aim to keep your reinforcement learning knowledge fresh by periodically reminding you of concepts making you a master of RL knowledge!!,other
2373,2020-09-25 22:41:22,GraphNorm,A Principled Approach to Accelerating Graph Neural Network Training.,other
2384,2020-09-27 08:42:46,Intro to Facebook Prophet,Everything you need to know when starting out with Facebook’s time series forecasting tool,other
2387,2020-09-27 14:22:51,GitHub Actions for Machine Learning,This presentation discusses the use of GitHub Actions to automate certain steps of a toy ML project. ,mlops
2388,2020-09-27 22:09:32,SemTorch,Different deep learning architectures definitions that can be applied to image segmentation.,computer-vision
2389,2020-09-28 05:34:15,bingoset - CLI tool to create image dataset.,CLI Toolkit to quickly create an image dataset using Bing Image Search API.,computer-vision
2395,2020-09-28 22:51:23,Python caching in GitHub Actions,How to speed up slow Python builds in GitHub Actions with effective caching.,mlops
2396,2020-09-29 00:36:12,EfficientDet meets Pytorch Lightning,Beginner friendly guide to object detection using EfficientDet.,computer-vision
2397,2020-09-29 02:15:46,Optimizing MobileDet for Mobile Deployments,Learn about the criticalities of effectively optimizing MobileDet object detectors for mobile deployments.,computer-vision
2402,2020-09-30 22:11:07,Adapting Text Augmentation to Industry Problems,"In this post I will talk about the recent advances in exploiting language models for data generation and also show how, where we can implement them in Industry.",natural-language-processing
2404,2020-09-30 22:22:07,12 Factors of Reproducible Machine Learning in Production,We took our experience to deduce 12 factors (as a nod to the 12 factor app) that build the backbone of successful ML in production.,mlops
2410,2020-10-01 13:42:23,Serving PyTorch models in production with the Amazon SageMaker,TorchServe is now natively supported in Amazon SageMaker as the default model server for PyTorch inference. ,mlops
2411,2020-10-01 14:55:12,How to Make Sense of the Reinforcement Learning Agents?,What and Why I Log During Training and Debug?,other
2412,2020-10-01 18:50:05,Introduction to 3D Medical Imaging: Preprocessing & Augmentations,"Learn how to apply 3D transformations for medical image preprocessing and augmentation, to setup your awesome deep learning pipeline.",computer-vision
2415,2020-10-01 23:55:36,Explainable ML Monitoring,"The video covers an overview of some of the risks of AI, the need for explainable monitoring, and what exactly we mean when we talk about it.",mlops
2417,2020-10-02 09:44:25,Parallelizing Prophet Cross-Validation with Dask,Applied Example w/ Code,other
2418,2020-10-02 10:16:17,Top Research Papers from the ECML-PKDD 2020 Conference,ECML-PKDD -> selectionof the best reaesch papers,other
2419,2020-10-02 15:37:27,GANs in Computer Vision Free Ebook / Article-series,This free ebook/article-series follows the chronological order of 20 peer-reviewed highly-cited papers as they presented in a series of 6 articles.,computer-vision
2422,2020-10-02 21:48:21,Pattern-Exploiting Training (PET),"This repository contains the code for ""Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference""",natural-language-processing
2423,2020-10-03 20:27:36,Imaginaire,NVIDIA PyTorch GAN library with distributed and mixed precision support.,computer-vision
2430,2020-10-05 10:09:28,Transection: Transformers for English to Chinese Translation 基于t,Tutorials on how to fine-tune a BART based transformer for English to Chinese translation.,natural-language-processing
2431,2020-10-05 12:36:02,A Survey of the State of Explainable AI for NLP,Overview of the operations and explainability techniques currently available for generating explanations for NLP model predictions.,natural-language-processing
2432,2020-10-05 13:09:58,Topic Modeling with BERT,Leveraging 🤗 Transformers and a class-based TF-IDF to create dense clusters allowing for easily interpretable topics. ,natural-language-processing
2434,2020-10-06 02:13:01,OpenMMLab Computer Vision,"MMCV is a python library for CV research and supports many research projects such as object detection, segmentation, pose estimation, action classification.
",computer-vision
2436,2020-10-06 13:29:44,Machine Learning Methods Explained (+ Examples),Most common techniques used in data science projects; get to know them through easy-to-understand examples and put them into practice in your own ML projects!,other
2437,2020-10-06 14:53:39,Rasoee,"A powerful web and mobile application that identifies food dishes from a given input image, and provides an ingredient list along with relevant recipes.",computer-vision
================================================
FILE: datasets/holdout.csv
================================================
id,created_on,title,description,tag
19,2020-03-03 13:54:31,Diffusion to Vector,Reference implementation of Diffusion2Vec (Complenet 2018) built on Gensim and NetworkX. ,other
26,2020-03-07 23:11:58,Graph Wavelet Neural Network,"A PyTorch implementation of ""Graph Wavelet Neural Network"" (ICLR 2019) ",other
44,2020-03-08 00:32:58,Capsule Graph Neural Network,"A PyTorch implementation of ""Capsule Graph Neural Network"" (ICLR 2019).",other
80,2020-03-20 05:59:32,NeRF: Neural Radiance Fields,Representing scenes as neural radiance fields for view synthesis.,computer-vision
84,2020-03-20 15:18:43,Mention Classifier,"Category prediction model
This repo contains AllenNLP model for prediction of Named Entity categories by its mentions.",natural-language-processing
107,2020-03-21 23:09:03,Plant Fruit Classifier,Building a world-class image classifier model with a custom dataset.,computer-vision
126,2020-03-25 15:05:27,Unet Implementation is Keras with GPU,Vector Map generation from aerial imagery using deep learning GeoSpatial UNET,computer-vision
130,2020-03-25 16:55:31,Gymnast Pose Analysis,"Pose modelling for gymnasts using open-pose and open-cv.
",computer-vision
131,2020-03-25 17:00:54,EfficientDet: Scalable and Efficient Object Detection,Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch.,computer-vision
136,2020-03-26 17:22:36,Finetune: Scikit-learn Style Model Finetuning for NLP,Finetune is a library that allows users to leverage state-of-the-art pretrained NLP models for a wide variety of downstream tasks.,natural-language-processing
141,2020-03-28 17:41:42,First Order Motion Model for Image Animation,Generating a video sequence so that an object in a source image is animated according to the motion of a driving video.,computer-vision
142,2020-03-28 17:49:20,TorchIO: Medical Image Processing in Deep Learning and PyTorch,Tools for medical image processing in deep learning and PyTorch,computer-vision
144,2020-03-29 18:23:06,Finetuning Transformers with JAX + Haiku,"Walking through a port of the RoBERTa pre-trained model to JAX + Haiku, then fine-tuning the model to solve a downstream task.",natural-language-processing
218,2020-04-06 11:29:57,Distributional RL using TensorFlow2,🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.,other
220,2020-04-06 15:19:59,Module 2: Convolutional Neural Networks - CS231n ,In Lecture 5 we move from fully-connected neural networks to convolutional neural networks.,computer-vision
249,2020-04-06 19:20:12,makesense.ai,Free to use online tool for labelling photos.,computer-vision
264,2020-04-06 21:33:32,The Unreasonable Effectiveness of Recurrent Neural Networks,A close look at how RNNs are able to perform so well.,natural-language-processing
268,2020-04-06 21:51:55,A Gentle Introduction to Text Summarization in Machine Learning,Text summarization is the technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful info.,natural-language-processing
285,2020-04-07 03:45:03,A (Long) Peek into Reinforcement Learning,"In this post, we are gonna briefly go over the field of Reinforcement Learning (RL), from fundamental concepts to classic algorithms.",other
305,2020-04-07 20:00:37,Question Answering with a Fine-Tuned BERT,What does it mean for BERT to achieve “human-level performance on Question Answering”?,natural-language-processing
314,2020-04-08 00:06:21,The Autonomous Learning Library,A PyTorch library for building deep reinforcement learning agents.,other
317,2020-04-08 00:14:27,COCO Annotator,"✏️ Web-based image segmentation tool for object detection, localization and key points.",computer-vision
328,2020-04-08 14:29:22,ProteinGCN: Protein model quality assessment using GCNs,Source code for the paper: ProteinGCN: Protein model quality assessment using Graph Convolutional Networks.,other
344,2020-04-08 16:11:28,Tokenizers,💥Fast State-of-the-Art Tokenizers optimized for Research and Production.,natural-language-processing
353,2020-04-08 17:08:41,Keras OCR,A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model. ,computer-vision
384,2020-04-08 21:22:25,Visualizing Memorization in RNNs,Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.,natural-language-processing
407,2020-04-08 23:00:02,AllenNLP,"An open-source NLP research library, built on PyTorch.",natural-language-processing
410,2020-04-08 23:09:15,Frameworks for Machine Learning Model Management,This blog post will follow up by comparing three different tools developed to support reproducible machine learning model development.,mlops
414,2020-04-08 23:18:04,TensorBoard.dev ,"Easily host, track, and share your ML experiments for free.",mlops
415,2020-04-08 23:21:13,BertViz,"Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)",natural-language-processing
426,2020-04-09 16:37:10,The Transformer Family,"This post presents how the vanilla Transformer can be improved for longer-term attention span, less memory and computation consumption, RL task solving, etc.",natural-language-processing
437,2020-04-10 17:14:11,Pruning Bert to Accelerate Inference,"After previously discussing various ways of accelerating models like BERT, in this blog post we empirically evaluate the pruning approach.",natural-language-processing
438,2020-04-10 17:26:39,Compressing Bert for Faster Prediction,"In this blog post, we discuss ways to make huge models like BERT smaller and faster. ",natural-language-processing
451,2020-04-10 20:10:28,Evaluation Metrics for Language Modeling,"In this article, we will focus on traditional intrinsic metrics that are extremely useful during the process of training the language model itself. ",natural-language-processing
454,2020-04-10 20:27:12,All The Ways You Can Compress BERT,In this post I’ll list and briefly taxonomize all the papers I’ve seen compressing BERT. ,natural-language-processing
458,2020-04-10 20:58:41,"Limitations of Deep Learning for Vision, and How We Might Fix The",This is an opinion paper about the strengths and weaknesses of Deep Nets for vision.,computer-vision
487,2020-04-14 21:15:35,Face Alignment in Full Pose Range: A 3D Total Solution,Face Alignment in Full Pose Range: A 3D Total Solution.,computer-vision
488,2020-04-14 21:21:51,V2V-PoseNet Pytorch,PyTorch implementation of V2V-PoseNet with IntegralPose/PoseFix loss.,computer-vision
496,2020-04-14 23:14:59,Fast- Neural Style,Pytorch implementation of an algorithm for artistic style transfer. ,computer-vision
497,2020-04-14 23:21:16,Torchvision Object Detection Finetuning Tutorial,Finetuning a pre-trained Mask R-CNN model in the Penn-Fudan Database for Pedestrian Detection and Segmentation.,computer-vision
559,2020-04-16 16:18:26,Creating an End-to-End Machine Learning Application,"A complete, end-to-end ML application, implemented in both TensorFlow 2.0 and PyTorch.",mlops
561,2020-04-16 16:27:31,How Docker Can Help You Become A More Effective Data Scientist,A look at Docker from the perspective of a data scientist.,mlops
569,2020-04-18 13:32:36,An Introduction to Transfer Learning and HuggingFace,In this talk I'll start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architect,natural-language-processing
570,2020-04-19 17:40:48,Introduction to Image Inpainting With Deep Learning,"In this article, we are going to learn how to do “image inpainting”, i.e. fill in missing parts of images precisely using deep learning.",computer-vision
579,2020-04-20 00:53:19,Transfer Learning & Fine-Tuning With Keras,Your 100% up-to-date guide to transfer learning & fine-tuning with Keras.,computer-vision
582,2020-04-20 21:38:50,CS285: Deep Reinforcement Learning,"A course on deep reinforcement learning, transfer and multi-task learning.",other
594,2020-04-21 23:25:53,TorchServe & TorchElastic PyTorch Libraries for Serving/Training,The officially supported way to deploy and manage models with PyTorch.,mlops
600,2020-04-22 17:37:25,Building a Simple Chatbot from Scratch in Python (using NLTK),A look at retrieval based and generative conversational AI for creating chatbots.,natural-language-processing
612,2020-04-23 13:56:46,Implementing DCGANs using PyTorch C++ API (Libtorch),"The blog discusses the paper review of DCGANs and implementation using PyTorch C++ API in detail. From loading models to visualizing batch of the data, in C++! ",computer-vision
620,2020-04-23 17:26:26,ELECTRA ,"Explaining the new self-supervised task for language representation learning, ELECTRA which uses ""replace token detection"".",natural-language-processing
624,2020-04-24 00:42:41,How to Train a New Language Model From Scratch Using Transformers,"In this post we’ll demo how to train a “small” model (84 M parameters = 6 layers, 768 hidden size, 12 attention heads).",natural-language-processing
629,2020-04-24 05:01:26,ARIMA Modeling - Guide to Time Series Forecasting in Python,"How ARIMA models works . How to train and forecast using ARIMA, SARIMA, SARIMAX and find the optimal model with Python",other
649,2020-04-28 03:42:29,Spektral,Graph Neural Networks with Keras and Tensorflow 2.,other
666,2020-04-29 12:10:43,AIDeveloper,"GUI-based software for training, evaluating and applying deep neural nets for image classification ",computer-vision
671,2020-04-29 23:22:43,MedCAT - Medical Concept Annotation Tool,A tool used to extract information from Electronic Health Records (EHRs) and link it to biomedical ontologies like SNOMED-CT and UMLS.,natural-language-processing
681,2020-05-01 16:25:34,The AI Economist,Improving Equality and Productivity with AI-Driven Tax Policies,other
684,2020-05-01 16:48:19,WT5?! Training Text-to-Text Models to Explain their Predictions,We leverage the text-to-text framework proposed by Raffel et al.(2019) to train language models to output a natural text explanation alongside their prediction.,natural-language-processing
689,2020-05-01 17:51:53,Ensemble Forecasts ,"Time series forecasting using classical methods (ETS, Holt-Winter's, SARIMA) and Prophet. I show and discuss advantages of Ensemble Forecast",other
703,2020-05-04 05:09:59,Implementing Graph Neural Networks with JAX,I’ll talk about my experience on how to build and train Graph Neural Networks (GNNs) with JAX.,other
705,2020-05-04 14:13:13,Deep Learning With Graph-Structured Representations,Novel approaches based on the theme of structuring the representations and computations of neural network-based models in the form of a graph.,other
706,2020-05-04 14:18:58,GNNExplainer: Generating Explanations for Graph Neural Networks,General tool for explaining predictions made by graph neural networks (GNNs).,other
710,2020-05-05 04:01:24,Differential Subspace Search in High-Dimensional Latent Space,"Differential subspace search to allow efficient iterative user exploration in such a space, without relying on domain- or data-specific assumptions.",computer-vision
723,2020-05-05 19:45:50,DeepWay: Autonomous navigation for blind.,I have tried to make something which can be used by blind people to navigate around the streets. Have a look at the video and GitHub repo for details.,computer-vision
737,2020-05-06 18:06:04,Nature-Scene Classification using FASTAI,Classifying Nature-scene images using deep learning with fastai library,computer-vision
738,2020-05-06 20:33:00,Machine-Learning-Single-Layer-Multiclass-Perceptron,Implemented a Single Layer Perceptron and applied it on the MNIST dataset for multi-class classification using NumPy.,computer-vision
780,2020-05-08 12:06:30,Med7 - clinical natural language processing for EHR,"Med7 is a transferable clinical natural language processing model for electronic health records, compatible with spaCy, for named-entity recognition task",natural-language-processing
784,2020-05-08 14:59:08,Haystack — Neural Question Answering At Scale,Scaling Question Answering models to find answers in large document stores via retriever and reader approach.,natural-language-processing
785,2020-05-08 17:13:36,SimCLR in TensorFlow 2,(Minimally) implements SimCLR (https://arxiv.org/abs/2002.05709) in TensorFlow 2.,computer-vision
787,2020-05-08 18:15:56,Semantic Cord19 Paper Explorer,Semantic research paper explorer to search Research Papers in COVID and CoronaVirus. Can be easily modified to any Research Paper Database,natural-language-processing
807,2020-05-11 02:25:51,Introduction to Machine Learning Problem Framing,This course helps you frame machine learning (ML) problems.,mlops
834,2020-05-13 04:36:33,TailorGAN: Making User-Defined Fashion Designs,Generate a photo-realistic image which combines the texture from reference A and the new attribute from reference B.,computer-vision
843,2020-05-13 14:49:21,T5 fine-tuning,A colab notebook to showcase how to fine-tune T5 model on various NLP tasks (especially non text-2-text tasks with text-2-text approach),natural-language-processing
854,2020-05-14 12:05:20,ASAP: Pooling for Graph Neural Network (AAAI 2020),ASAP is a sparse and differentiable pooling method that addresses the limitations of previous graph pooling layers.,other
878,2020-05-16 05:27:56,Exploratory Data Analysis on MS COCO Style Datasets,A Simple Toolkit to do exploratory data analysis on MS COCO style formatted datasets.,computer-vision
898,2020-05-17 05:11:22,Single-Stage Semantic Segmentation from Image Labels,"We attain competitive results by training a single network model
for segmentation in a self-supervised fashion using only
image-level annotations",computer-vision
906,2020-05-18 14:50:45,NLPAug,Data augmentation for NLP,natural-language-processing
916,2020-05-19 08:11:05,Get Subreddit Suggestions for a Post,"Trained on 4M Reddit posts from 4k Subreddits. End-to-end ML pipeline built with fasttext and FastAPI, deployed to Valohai.",natural-language-processing
917,2020-05-19 13:45:03,Transfer Learning In NLP,A brief history of Transfer Learning In NLP,natural-language-processing
919,2020-05-20 02:29:48,IntelliCode Compose: Code Generation Using Transformer,"Code completion tool which is capable of predicting sequences of code tokens of arbitrary types, generating up to entire lines of syntactically correct code.",natural-language-processing
943,2020-05-22 06:27:43,Transfer Learning in NLP with Tensorflow Hub and Keras,Learn how to integrate and finetune tensorflow-hub modules in Tensorflow 2.0,natural-language-processing
946,2020-05-22 07:57:14,Replicating Airbnb's Amenity Detection (documentary series),Airbnb's engineering team shared an article on how they used computer vision to detection amenities in photos. It read like a recipe so I replicated it.,computer-vision
965,2020-05-24 08:14:30,GANs in Computer Vision : An article review series ,"An article series where we review the most important research papers on GANs from 2015 to today. 6 articles, 20 papers, 20000 words",computer-vision
991,2020-05-27 05:09:20,NLP Viewer 🤗,A simple website for browsing popular NLP datasets.,natural-language-processing
999,2020-05-28 03:32:05,MediaPipe,"Simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web. ",computer-vision
1011,2020-05-29 02:57:44,ML in Production - Deployment Series,"A multi-part blog series on deploying machine learning models in an automated, reproducible, and auditable manner.",mlops
1019,2020-05-29 08:14:05,Visual Object Tracking using Adaptive Correlation Filters,This article gives step by step tutorial with code on understanding MOSSE tracking algorithm,computer-vision
1032,2020-05-29 14:50:28,Pix2Pix with Tf-js,"Implementation of web friendly ML models using TensorFlow.js. pix2pix, face segmentation, fast style transfer and many more ...",computer-vision
1056,2020-05-30 09:08:31,Font Recognition Using Deep Learning - DeepFont ( Adobe ),DeepFont Paper is a technique created by Adobe.Inc to detect font from images using deep learning . They published their work as a paper for the public .,computer-vision
1078,2020-05-31 05:04:44,Building Footprint Extraction,The project retrieves satellite imagery from Google and performs building footprint extraction using a U-Net. ,computer-vision
1114,2020-06-01 21:00:24,Reinforcement Learning in JAX,"Implementation of interesting Deep Reinforcement Learning Algorithms using JAX based libraries (flax, haiku and rlax) As of now tasks come from OpenAI gym",other
1155,2020-06-03 15:22:11,GaborNet,Modified network architecture that focuses on improving convergence and reducing training complexity.,computer-vision
1159,2020-06-03 18:17:01,Learning To Classify Images Without Labels,A two-step approach where feature learning and clustering are decoupled.,computer-vision
1167,2020-06-04 03:58:21,From Pre-trained Word Embeddings to Pre-trained Language Models,from Static Word Embedding to Dynamic (Contextualized) Word Embedding.,natural-language-processing
1172,2020-06-04 07:01:13,Converting images to TF Records,A Colab Notebook showing how to convert an image dataset (for classification) to TF Records and more.,computer-vision
1266,2020-06-09 16:09:08,Text Classification using Bert from Tensorflow-Hub,This Tutorial helps to learn about Bert Models for Classification task on a #Tweet dataset.,natural-language-processing
1286,2020-06-10 17:24:19,Exploring Knowledge Captured in Probability of Strings,An exploration of simple knowledge captured by language models with code examples,natural-language-processing
1363,2020-06-13 13:46:44,Short Notes on Batch Constrained Deep Reinforcement Learning,Blog article on Off-Policy Deep Reinforcement Learning without Exploration paper by Fujimoto et al. (ICML 2019),other
1426,2020-06-15 02:34:27,From GRU to Transformer,How recurrent units and self-attention are related to each other.,natural-language-processing
1430,2020-06-15 04:24:12,Melanoma Classification,This was Shubhamai 3-week project for working a new kaggle competition and deploying a web application to predicting benign or malignant based on images.,computer-vision
1434,2020-06-15 07:52:13,Universal Sentence Encoder Visually Explained,A deep-dive into how Universal Sentence Encoder learns to generate fixed-length sentence embeddings,natural-language-processing
1445,2020-06-15 17:49:16,Image Smoothing via L0 Gradient Minimization,This is a edge-aware image smoothing algorithm. This algorithm tries to smoothen the image while preserving the global structural information of the image. ,computer-vision
1450,2020-06-15 21:00:47,BERT NLP — How To Build a Question Answering Bot,Understanding the intuition with hands-on PyTorch code for BERT fine-tuned on SQuAD.,natural-language-processing
1451,2020-06-16 01:21:09,EfficientDet (PyTorch),A PyTorch implementation of EfficientDet faithful to the original Google implementation with ported weights.,computer-vision
1459,2020-06-16 03:06:10,SuperGlue: Learning Feature Matching with Graph Neural Networks,"SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.",other
1462,2020-06-16 03:28:40,Open Compound Domain Adaptation,"Pytorch implementation for ""Open Compound Domain Adaptation""",computer-vision
1485,2020-06-17 16:33:50,Sudoku-Game-Solver,This is a Computer Vision Application that solves a 9x9 sudoku board game using Deep Learning and Backtracking algorithm.,computer-vision
1488,2020-06-17 19:27:36,Smart Picture Editor,Tool to automatically remove unwanted objects from photos,computer-vision
1494,2020-06-18 00:14:40,Object Goal Navigation using Goal-oriented Semantic Exploration,Embodied interactive learning for object detection by using semantic curiosity to learn an exploration policy on set of the training environments.,computer-vision
1501,2020-06-18 18:17:18,Traffic-Sign-Recognition-Using-Deep-Learning,"The training dataset contains around 39,000 images while test dataset contains around 12,000 images containing 43 different classes. We will be using Convolutio",computer-vision
1508,2020-06-19 06:43:47,Long Form Question Answering with ELI5,A model for open domain long form question answering.,natural-language-processing
1511,2020-06-19 06:54:23,RepNet - Class Agnostic Video Repetition Counting in the Wild,Counting Out Time: Class Agnostic Video Repetition Counting in the Wild,computer-vision
1515,2020-06-19 16:37:10,"Cut, Paste and Learn: Surprisingly Easy Synthesis for Detection",Generate synthetic scenes and bounding box annotations for object detection.,computer-vision
1524,2020-06-20 10:42:25,Machine Learning Projects ,"This Repo contains projects done by me while learning the basics. All the familiar types of regression, classification, and clustering methods have been used.",natural-language-processing
1540,2020-06-21 13:03:19,codeBERT - Masked Language Model for source code ,Tutorial to use codeBERT a MLM for Python code. Model trained from scratch using roBERTa,natural-language-processing
1588,2020-06-24 03:29:51,Multi-task Training with Hugging Face Transformers and NLP, A recipe for multi-task training with Transformers' Trainer and NLP datasets.,natural-language-processing
1600,2020-06-25 00:45:26,BERT Distillation with Catalyst,How to distill BERT with Catalyst.,natural-language-processing
1628,2020-06-28 06:12:20,Deep Reinforcement Learning Amidst Lifelong Non-Stationarity,"How can robots learn in changing, open-world environments? We introduce dynamic-parameter MDPs, to capture environments with persistent, unobserved changes. ",other
1654,2020-06-30 03:58:46,3D Detection and Domain Adaptation,1st Place Solution for Waymo Open Dataset Challenge,computer-vision
1659,2020-07-01 02:26:20,Evaluation of Text Generation: A Survey,Evaluation methods of natural language generation (NLG) and language modeling.,natural-language-processing
1661,2020-07-01 06:42:59,SpineNet: A Novel Architecture for Object Detection,"A meta architecture called a scale-permuted model that enables two major improvements on backbone architecture design,iscovered with neural architecture search.",computer-vision
1665,2020-07-01 07:17:48,BERTology Meets Biology,Interpreting Attention in Protein Language Models.,natural-language-processing
1681,2020-07-03 04:02:52,A Survey on Deep Learning for Localization and Mapping,Towards the Age of Spatial Machine Intelligence,computer-vision
1685,2020-07-03 04:12:28,Text Data Cleanup - Dynamic Embedding Visualisation,Identify noisy text in a Machine Translation dataset through dynamic text embedding visualisation.,natural-language-processing
1689,2020-07-03 04:29:04,Offline Reinforcement Learning,"Challenges, algorithms and benchmarks.",other
1692,2020-07-03 04:42:45,Low-Dimensional Hyperbolic Knowledge Graph Embeddings,Low-dimensional knowledge graph embeddings that simultaneously capture hierarchical relations and logical patterns.,other
1703,2020-07-04 09:22:50,Awesome Deep RL,This project is built for people who are learning and researching on the latest deep reinforcement learning methods.,other
1709,2020-07-05 05:25:34,Anti-Patterns in NLP (8 types of NLP idiots),A talk which discusses the recurring industrial problems in making NLP solutions. ,natural-language-processing
1715,2020-07-06 18:25:16,Image Classifier,Pure JavaScript Image Classifier,computer-vision
1717,2020-07-07 04:09:35,TaBERT,Pretraining for Joint Understanding of Textual and Tabular Data,natural-language-processing
1719,2020-07-07 04:17:11,Texthero,"Text preprocessing, representation and visualization from zero to hero.",natural-language-processing
1743,2020-07-09 01:51:41,How to Benchmark Models with Transformers,HuggingFace's Transformer library allows users to benchmark models for both TensorFlow 2 and PyTorch using the PyTorchBenchmark and TensorFlowBenchmark classes.,natural-language-processing
1756,2020-07-10 02:53:13,Linear Attention Transformer,A fully featured Transformer that mixes (QKᵀ)V local attention with Q(KᵀV) global attention (scales linearly with respect to sequence length).,natural-language-processing
1770,2020-07-11 05:12:49,imgaug,"Image augmentation for machine learning experiments.
",computer-vision
1779,2020-07-11 05:48:03,All Models and checkpoints - Hugging Face,"Massive (and growing) collection of NLP models are nearly any NLP tasks, especially those involving the use of transformers.",natural-language-processing
1799,2020-07-11 06:49:38,FlashText,"Extract Keywords from sentence or Replace keywords in sentences.
",natural-language-processing
1804,2020-07-11 07:04:25,Text Preprocessing in Python using spaCy libra
gitextract_lbngfpom/
├── .github/
│ └── workflows/
│ ├── documentation.yaml
│ ├── json_to_md.py
│ ├── serve.yaml
│ └── workloads.yaml
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE
├── Makefile
├── README.md
├── datasets/
│ ├── dataset.csv
│ ├── holdout.csv
│ ├── projects.csv
│ └── tags.csv
├── deploy/
│ ├── cluster_compute.yaml
│ ├── cluster_env.yaml
│ ├── jobs/
│ │ ├── workloads.sh
│ │ └── workloads.yaml
│ └── services/
│ ├── serve_model.py
│ └── serve_model.yaml
├── docs/
│ ├── index.md
│ └── madewithml/
│ ├── data.md
│ ├── evaluate.md
│ ├── models.md
│ ├── predict.md
│ ├── serve.md
│ ├── train.md
│ ├── tune.md
│ └── utils.md
├── madewithml/
│ ├── __init__.py
│ ├── config.py
│ ├── data.py
│ ├── evaluate.py
│ ├── models.py
│ ├── predict.py
│ ├── serve.py
│ ├── train.py
│ ├── tune.py
│ └── utils.py
├── mkdocs.yml
├── notebooks/
│ ├── benchmarks.ipynb
│ ├── clear_cell_nums.py
│ └── madewithml.ipynb
├── pyproject.toml
├── requirements.txt
└── tests/
├── code/
│ ├── conftest.py
│ ├── test_data.py
│ ├── test_predict.py
│ ├── test_train.py
│ ├── test_tune.py
│ ├── test_utils.py
│ └── utils.py
├── data/
│ ├── conftest.py
│ └── test_dataset.py
└── model/
├── conftest.py
├── test_behavioral.py
└── utils.py
SYMBOL INDEX (85 symbols across 22 files)
FILE: .github/workflows/json_to_md.py
function to_markdown (line 5) | def to_markdown(data):
function json_to_markdown (line 38) | def json_to_markdown(json_fp, md_fp):
FILE: madewithml/data.py
function load_data (line 14) | def load_data(dataset_loc: str, num_samples: int = None) -> Dataset:
function stratify_split (line 30) | def stratify_split(
function clean_text (line 77) | def clean_text(text: str, stopwords: List = STOPWORDS) -> str:
function tokenize (line 104) | def tokenize(batch: Dict) -> Dict:
function preprocess (line 118) | def preprocess(df: pd.DataFrame, class_to_index: Dict) -> Dict:
class CustomPreprocessor (line 137) | class CustomPreprocessor:
method __init__ (line 140) | def __init__(self, class_to_index={}):
method fit (line 144) | def fit(self, ds):
method transform (line 150) | def transform(self, ds):
FILE: madewithml/evaluate.py
function get_overall_metrics (line 23) | def get_overall_metrics(y_true: np.ndarray, y_pred: np.ndarray) -> Dict:...
function get_per_class_metrics (line 43) | def get_per_class_metrics(y_true: np.ndarray, y_pred: np.ndarray, class_...
function nlp_llm (line 68) | def nlp_llm(x): # pragma: no cover, eval workload
function short_text (line 77) | def short_text(x): # pragma: no cover, eval workload
function get_slice_metrics (line 82) | def get_slice_metrics(y_true: np.ndarray, y_pred: np.ndarray, ds: Datase...
function evaluate (line 109) | def evaluate(
FILE: madewithml/models.py
class FinetunedLLM (line 11) | class FinetunedLLM(nn.Module):
method __init__ (line 12) | def __init__(self, llm, dropout_p, embedding_dim, num_classes):
method forward (line 21) | def forward(self, batch):
method predict (line 29) | def predict(self, batch):
method predict_proba (line 36) | def predict_proba(self, batch):
method save (line 42) | def save(self, dp):
method load (line 53) | def load(cls, args_fp, state_dict_fp):
FILE: madewithml/predict.py
function decode (line 23) | def decode(indices: Iterable[Any], index_to_class: Dict) -> List:
function format_prob (line 36) | def format_prob(prob: Iterable, index_to_class: Dict) -> Dict:
class TorchPredictor (line 52) | class TorchPredictor:
method __init__ (line 53) | def __init__(self, preprocessor, model):
method __call__ (line 58) | def __call__(self, batch):
method predict_proba (line 62) | def predict_proba(self, batch):
method get_preprocessor (line 66) | def get_preprocessor(self):
method from_checkpoint (line 70) | def from_checkpoint(cls, checkpoint):
function predict_proba (line 77) | def predict_proba(
function get_best_run_id (line 102) | def get_best_run_id(experiment_name: str = "", metric: str = "", mode: s...
function get_best_checkpoint (line 122) | def get_best_checkpoint(run_id: str) -> TorchCheckpoint: # pragma: no c...
function predict (line 137) | def predict(
FILE: madewithml/serve.py
class ModelDeployment (line 24) | class ModelDeployment:
method __init__ (line 25) | def __init__(self, run_id: str, threshold: int = 0.9):
method _index (line 34) | def _index(self) -> Dict:
method _run_id (line 44) | def _run_id(self) -> Dict:
method _evaluate (line 49) | async def _evaluate(self, request: Request) -> Dict:
method _predict (line 55) | async def _predict(self, request: Request):
FILE: madewithml/train.py
function train_step (line 36) | def train_step(
function eval_step (line 71) | def eval_step(
function train_loop_per_worker (line 101) | def train_loop_per_worker(config: dict) -> None: # pragma: no cover, te...
function train_model (line 152) | def train_model(
FILE: madewithml/tune.py
function tune_models (line 30) | def tune_models(
FILE: madewithml/utils.py
function set_seeds (line 16) | def set_seeds(seed: int = 42):
function load_dict (line 27) | def load_dict(path: str) -> Dict:
function save_dict (line 41) | def save_dict(d: Dict, path: str, cls: Any = None, sortkeys: bool = Fals...
function pad_array (line 58) | def pad_array(arr: np.ndarray, dtype=np.int32) -> np.ndarray:
function collate_fn (line 76) | def collate_fn(batch: Dict[str, np.ndarray]) -> Dict[str, torch.Tensor]:...
function get_run_id (line 94) | def get_run_id(experiment_name: str, trial_id: str) -> str: # pragma: n...
function dict_to_list (line 109) | def dict_to_list(data: Dict, keys: List[str]) -> List[Dict[str, Any]]:
FILE: notebooks/clear_cell_nums.py
function clear_execution_numbers (line 6) | def clear_execution_numbers(nb_path):
FILE: tests/code/conftest.py
function dataset_loc (line 7) | def dataset_loc():
function preprocessor (line 12) | def preprocessor():
FILE: tests/code/test_data.py
function df (line 9) | def df():
function class_to_index (line 16) | def class_to_index():
function test_load_data (line 21) | def test_load_data(dataset_loc):
function test_stratify_split (line 27) | def test_stratify_split():
function test_clean_text (line 45) | def test_clean_text(text, sw, clean_text):
function test_preprocess (line 49) | def test_preprocess(df, class_to_index):
function test_fit_transform (line 55) | def test_fit_transform(dataset_loc, preprocessor):
FILE: tests/code/test_predict.py
function test_decode (line 4) | def test_decode():
function test_format_prob (line 9) | def test_format_prob():
FILE: tests/code/test_train.py
function test_train_model (line 10) | def test_train_model(dataset_loc):
FILE: tests/code/test_tune.py
function test_tune_models (line 10) | def test_tune_models(dataset_loc):
FILE: tests/code/test_utils.py
function test_set_seed (line 12) | def test_set_seed():
function test_save_and_load_dict (line 23) | def test_save_and_load_dict():
function test_pad_array (line 32) | def test_pad_array():
function test_collate_fn (line 38) | def test_collate_fn():
function test_dict_to_list (line 61) | def test_dict_to_list(d, keys, list):
FILE: tests/code/utils.py
function generate_experiment_name (line 6) | def generate_experiment_name(prefix: str = "test") -> str:
function delete_experiment (line 10) | def delete_experiment(experiment_name: str) -> None:
FILE: tests/data/conftest.py
function pytest_addoption (line 6) | def pytest_addoption(parser):
function df (line 14) | def df(request):
FILE: tests/data/test_dataset.py
function test_dataset (line 1) | def test_dataset(df):
FILE: tests/model/conftest.py
function pytest_addoption (line 7) | def pytest_addoption(parser):
function run_id (line 12) | def run_id(request):
function predictor (line 17) | def predictor(run_id):
FILE: tests/model/test_behavioral.py
function test_invariance (line 15) | def test_invariance(input_a, input_b, label, predictor):
function test_directional (line 39) | def test_directional(input, label, predictor):
function test_mft (line 62) | def test_mft(input, label, predictor):
FILE: tests/model/utils.py
function get_label (line 6) | def get_label(text, predictor):
Condensed preview — 56 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (3,393K chars).
[
{
"path": ".github/workflows/documentation.yaml",
"chars": 482,
"preview": "name: documentation\non:\n push:\n branches:\n - main\n\njobs:\n build-docs:\n runs-on: ubuntu-22.04\n steps:\n "
},
{
"path": ".github/workflows/json_to_md.py",
"chars": 2272,
"preview": "import json\nimport sys\n\n\ndef to_markdown(data):\n markdown = \"\"\n for key, value in data.items():\n markdown +"
},
{
"path": ".github/workflows/serve.yaml",
"chars": 976,
"preview": "name: serve\non:\n workflow_dispatch: # manual\n push:\n branches:\n - main\npermissions: write-all\n\njobs:\n serve:\n "
},
{
"path": ".github/workflows/workloads.yaml",
"chars": 1738,
"preview": "name: workloads\non:\n workflow_dispatch: # manual\n pull_request:\n branches:\n - main\npermissions: write-all\n\njobs"
},
{
"path": ".gitignore",
"chars": 1042,
"preview": "# Data\nlogs/\nstores/\nmlflow/\nresults/\nworkspaces/\nefs/\n\n# VSCode\n.vscode/\n.idea\n\n# Byte-compiled / optimized / DLL files"
},
{
"path": ".pre-commit-config.yaml",
"chars": 627,
"preview": "# See https://pre-commit.com for more information\n# See https://pre-commit.com/hooks.html for more hooks\nrepos:\n- repo"
},
{
"path": "LICENSE",
"chars": 1069,
"preview": "MIT License\n\nCopyright (c) 2023 Made With ML\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "Makefile",
"chars": 415,
"preview": "# Makefile\nSHELL = /bin/bash\n\n# Styling\n.PHONY: style\nstyle:\n\tblack .\n\tflake8\n\tpython3 -m isort .\n\tpyupgrade\n\n# Cleaning"
},
{
"path": "README.md",
"chars": 23434,
"preview": "<div align=\"center\">\n<h1><img width=\"30\" src=\"https://madewithml.com/static/images/rounded_logo.png\"> <a href=\"http"
},
{
"path": "datasets/dataset.csv",
"chars": 141766,
"preview": "id,created_on,title,description,tag\n6,2020-02-20 06:43:18,Comparison between YOLO and RCNN on real world videos,Bringing"
},
{
"path": "datasets/holdout.csv",
"chars": 35460,
"preview": "id,created_on,title,description,tag\n19,2020-03-03 13:54:31,Diffusion to Vector,Reference implementation of Diffusion2Vec"
},
{
"path": "datasets/projects.csv",
"chars": 127500,
"preview": "id,created_on,title,description\n6,2020-02-20 06:43:18,Comparison between YOLO and RCNN on real world videos,Bringing the"
},
{
"path": "datasets/tags.csv",
"chars": 15488,
"preview": "tag\ncomputer-vision\ncomputer-vision\ngraph-learning\nreinforcement-learning\ngraph-learning\ngraph-learning\ngraph-learning\ng"
},
{
"path": "deploy/cluster_compute.yaml",
"chars": 464,
"preview": "cloud: education-us-west-2\nregion: us-west-2\nhead_node_type:\n name: head_node_type\n instance_type: g5.4xlarge\nworker_n"
},
{
"path": "deploy/cluster_env.yaml",
"chars": 328,
"preview": "base_image: anyscale/ray:2.7.0optimized-py310-cu118\nenv_vars: {}\ndebian_packages:\n - curl\n\npython:\n pip_packages: []\n "
},
{
"path": "deploy/jobs/workloads.sh",
"chars": 2047,
"preview": "#!/bin/bash\nexport PYTHONPATH=$PYTHONPATH:$PWD\nmkdir results\n\n# Test data\nexport RESULTS_FILE=results/test_data_results."
},
{
"path": "deploy/jobs/workloads.yaml",
"chars": 402,
"preview": "name: workloads\nproject_id: prj_wn6el5cu9dqwktk6t4cv54x8zh\ncluster_env: madewithml-cluster-env\ncompute_config: madewithm"
},
{
"path": "deploy/services/serve_model.py",
"chars": 624,
"preview": "import os\nimport subprocess\nimport sys\n\nsys.path.append(\".\")\n\nfrom madewithml.config import MODEL_REGISTRY # NOQA: E402"
},
{
"path": "deploy/services/serve_model.yaml",
"chars": 480,
"preview": "name: madewithml\nproject_id: prj_wn6el5cu9dqwktk6t4cv54x8zh\ncluster_env: madewithml-cluster-env\ncompute_config: madewith"
},
{
"path": "docs/index.md",
"chars": 376,
"preview": "## Documentation\n\n- [madewithml](madewithml/data.md): documentation.\n\n## Lessons\n\nLearn how to combine machine learning "
},
{
"path": "docs/madewithml/data.md",
"chars": 20,
"preview": "::: madewithml.data\n"
},
{
"path": "docs/madewithml/evaluate.md",
"chars": 24,
"preview": "::: madewithml.evaluate\n"
},
{
"path": "docs/madewithml/models.md",
"chars": 22,
"preview": "::: madewithml.models\n"
},
{
"path": "docs/madewithml/predict.md",
"chars": 23,
"preview": "::: madewithml.predict\n"
},
{
"path": "docs/madewithml/serve.md",
"chars": 21,
"preview": "::: madewithml.serve\n"
},
{
"path": "docs/madewithml/train.md",
"chars": 21,
"preview": "::: madewithml.train\n"
},
{
"path": "docs/madewithml/tune.md",
"chars": 20,
"preview": "::: madewithml.tune\n"
},
{
"path": "docs/madewithml/utils.md",
"chars": 21,
"preview": "::: madewithml.utils\n"
},
{
"path": "madewithml/__init__.py",
"chars": 46,
"preview": "from dotenv import load_dotenv\n\nload_dotenv()\n"
},
{
"path": "madewithml/config.py",
"chars": 4212,
"preview": "# config.py\nimport logging\nimport os\nimport sys\nfrom pathlib import Path\n\nimport mlflow\n\n# Directories\nROOT_DIR = Path(_"
},
{
"path": "madewithml/data.py",
"chars": 5666,
"preview": "import re\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport ray\nfrom ray.data import D"
},
{
"path": "madewithml/evaluate.py",
"chars": 5632,
"preview": "import datetime\nimport json\nfrom collections import OrderedDict\nfrom typing import Dict\n\nimport numpy as np\nimport ray\ni"
},
{
"path": "madewithml/models.py",
"chars": 1911,
"preview": "import json\nimport os\nfrom pathlib import Path\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom "
},
{
"path": "madewithml/predict.py",
"chars": 5393,
"preview": "import json\nfrom pathlib import Path\nfrom typing import Any, Dict, Iterable, List\nfrom urllib.parse import urlparse\n\nimp"
},
{
"path": "madewithml/serve.py",
"chars": 2628,
"preview": "import argparse\nimport os\nfrom http import HTTPStatus\nfrom typing import Dict\n\nimport ray\nfrom fastapi import FastAPI\nfr"
},
{
"path": "madewithml/train.py",
"chars": 10419,
"preview": "import datetime\nimport json\nimport os\nimport tempfile\nfrom typing import Tuple\n\nimport numpy as np\nimport ray\nimport ray"
},
{
"path": "madewithml/tune.py",
"chars": 7114,
"preview": "import datetime\nimport json\nimport os\n\nimport ray\nimport typer\nfrom ray import tune\nfrom ray.air.config import (\n Che"
},
{
"path": "madewithml/utils.py",
"chars": 3824,
"preview": "import json\nimport os\nimport random\nfrom typing import Any, Dict, List\n\nimport numpy as np\nimport torch\nfrom ray.data im"
},
{
"path": "mkdocs.yml",
"chars": 520,
"preview": "site_name: Made With ML\nsite_url: https://madewithml.com/\nrepo_url: https://github.com/GokuMohandas/Made-With-ML/\nnav:\n "
},
{
"path": "notebooks/benchmarks.ipynb",
"chars": 158798,
"preview": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"abb04d61-c097-45a9-9201-e3649cbdc0cc\",\n "
},
{
"path": "notebooks/clear_cell_nums.py",
"chars": 707,
"preview": "from pathlib import Path\n\nimport nbformat\n\n\ndef clear_execution_numbers(nb_path):\n with open(nb_path, \"r\", encoding=\""
},
{
"path": "notebooks/madewithml.ipynb",
"chars": 2565698,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"acbetMKBt825\"\n },\n \"source\": [\n \"<div a"
},
{
"path": "pyproject.toml",
"chars": 833,
"preview": "# Black formatting\n[tool.black]\nline-length = 150\ninclude = '\\.pyi?$'\nexclude = '''\n/(\n .eggs # exclude a f"
},
{
"path": "requirements.txt",
"chars": 687,
"preview": "# Default\nhyperopt==0.2.7\nipywidgets>=8\nmatplotlib==3.7.1\nmlflow==2.3.1\nnltk==3.8.1\nnumpy==1.24.3\nnumpyencoder==0.3.0\npa"
},
{
"path": "tests/code/conftest.py",
"chars": 268,
"preview": "import pytest\n\nfrom madewithml.data import CustomPreprocessor\n\n\n@pytest.fixture\ndef dataset_loc():\n return \"https://r"
},
{
"path": "tests/code/test_data.py",
"chars": 1745,
"preview": "import pandas as pd\nimport pytest\nimport ray\n\nfrom madewithml import data\n\n\n@pytest.fixture(scope=\"module\")\ndef df():\n "
},
{
"path": "tests/code/test_predict.py",
"chars": 312,
"preview": "from madewithml import predict\n\n\ndef test_decode():\n decoded = predict.decode(indices=[0, 1, 1], index_to_class={0: \""
},
{
"path": "tests/code/test_train.py",
"chars": 835,
"preview": "import json\n\nimport pytest\nimport utils\n\nfrom madewithml import train\n\n\n@pytest.mark.training\ndef test_train_model(datas"
},
{
"path": "tests/code/test_tune.py",
"chars": 920,
"preview": "import json\n\nimport pytest\nimport utils\n\nfrom madewithml import tune\n\n\n@pytest.mark.training\ndef test_tune_models(datase"
},
{
"path": "tests/code/test_utils.py",
"chars": 1811,
"preview": "import tempfile\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nimport torch\nfrom ray.train.torch import get_"
},
{
"path": "tests/code/utils.py",
"chars": 395,
"preview": "import uuid\n\nfrom madewithml.config import mlflow\n\n\ndef generate_experiment_name(prefix: str = \"test\") -> str:\n retur"
},
{
"path": "tests/data/conftest.py",
"chars": 544,
"preview": "import great_expectations as ge\nimport pandas as pd\nimport pytest\n\n\ndef pytest_addoption(parser):\n \"\"\"Add option to s"
},
{
"path": "tests/data/test_dataset.py",
"chars": 978,
"preview": "def test_dataset(df):\n \"\"\"Test dataset quality and integrity.\"\"\"\n column_list = [\"id\", \"created_on\", \"title\", \"des"
},
{
"path": "tests/model/conftest.py",
"chars": 529,
"preview": "import pytest\n\nfrom madewithml import predict\nfrom madewithml.predict import TorchPredictor\n\n\ndef pytest_addoption(parse"
},
{
"path": "tests/model/test_behavioral.py",
"chars": 1837,
"preview": "import pytest\nimport utils\n\n\n@pytest.mark.parametrize(\n \"input_a, input_b, label\",\n [\n (\n \"Trans"
},
{
"path": "tests/model/utils.py",
"chars": 274,
"preview": "import ray\n\nfrom madewithml import predict\n\n\ndef get_label(text, predictor):\n sample_ds = ray.data.from_items([{\"titl"
}
]
About this extraction
This page contains the full source code of the GokuMohandas/madewithml GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 56 files (3.0 MB), approximately 787.8k tokens, and a symbol index with 85 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.