Repository: NirantK/awesome-project-ideas
Branch: master
Commit: 31f6fb0dba79
Files: 5
Total size: 16.9 KB
Directory structure:
gitextract_p5v86si0/
├── .gitignore
├── LICENSE
├── PULL_REQUEST_TEMPLATE.md
├── README.md
└── _config.yml
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# dotenv
.env
# virtualenv
.venv
venv/
ENV/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2017-2019 Nirant Kasliwal
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: PULL_REQUEST_TEMPLATE.md
================================================
DELETE EVERYTHING FROM THIS LINE AND BELOW
Your idea should be in this template:
**Main Idea Title**
- Ask a question here
- Dataset:
- (Optional) Getting Started
Here is an example:
**Music Genre recognition using neural networks**
- Can you identify the musical genre using their spectrograms or other sound information?
- Datasets: [FMA](https://github.com/mdeff/fma) or GTZAN on Keras
- Get started with [Librosa](https://librosa.github.io/librosa/index.html) for feature extraction
================================================
FILE: README.md
================================================
<!-- markdownlint-disable MD033 -->
# Awesome Deep Learning Project Ideas
[](https://github.com/sindresorhus/awesome)
A curated list of practical deep learning and machine learning project ideas
- 30+ ideas
- Relevant to both the academia and industry
- Ranges from beginner friendly to research projects
---
## Contents
- [Hackathon Ideas](#hackathon-ideas) - Project ideas unlocked by use of Large Language Models, specially text to text -- note that a lot of the text to text ideas can also be buit a lot better with LLMs now!
- [Text](#text) - With some topics about Natural language processing
- [Forecasting](#forecasting) - Most of the topics in this section is about Time Series and similar forecasting challenges
- [Recommendation Systems](#recommendation-systems)
- [Vision](#vision) - With topics about image and video processing
- [Music and Audio](#music) - These topics are about combining ideas from language and audio to understand music
- [Conclusion](#conclusion)
---
## Hackathon Ideas
- **Developer Ideas**
- Text to cmd for terminal: Take user intent in terminal e.g.
```bash
$ask "how to list all files with details"
> Execute "ls -l"? [y/N] y
$ls -l
```
- Build and edit YAMLs using natural language e.g. Kubernetes and other form of config files
- [Kor](eyurtsev.github.io/kor/) for ideas on how this is done for JSON
- Can be use-case specific. Build pipelines? Kube?
- Mobile Android/iOS SDK for Stable Diffusion inference
- Apple has released a [CoreML Stable Diffusion Inference](https://github.com/apple/ml-stable-diffusion)
- **Voice powered Experiences**
- Audio Conversation with chatGPT, can combine with fast Text-to-Speech e.g. [Eleven Labs](https://elevenlabs.io) to have a two-way conversation
- Telegram/WhatsApp bot to get audio and save as text with metadata into mem.ai or Roam Research or Obsidian
- Edit image by giving instructions of what you want to do: [SeeChatGPT](https://github.com/Nischaydnk/SeeChatGPT) and [playgroundai.com](playgroundai.com) as examples
- The underlying mechanism which you can use is called [InstructPix2Pix](huggingface.co/spaces/timbrooks/instruct-pix2pix)
- Semantic search over any media
- Can build using CLIP or [BLIP-2 embeddings](huggingface.co/docs/transformers/main/model_doc/blip-2) for images and [CLAP](https://github.com/LAION-AI/CLAP/tree/clap#quick-start) for all audio including music and speech
- Text to Music Generation
- See [MusicLM](https://google-research.github.io/seanet/musiclm/examples/) for reference
- **Knowledge Base QA** aka Answer Engines
- Take any plaintext dataset e.g. State of the Union address and build on top of that
- Can use this over Video Subtitles to search and QA over videos as well, by mapping back to source
- **Guided Summarisation/Rewriting**
- Take specific questions which the user might have about a large text dataset e.g. a novel or book and include that in your summary of the piece
- Pay attention to specific entities and retell the events which happen in a story with attention to that character
- **ControlNet + Stable Diffusion for Aethetic Control**
- Build tooling using [diffusers](https://github.com/huggingface/diffusers/) which takes in a set of photos, finetunes a model (LoRA) on a person, detects face and moves it to a new aesthetic e.g. futuristic neon punk, grunge rock, Studio Ghibli. Can also add InstructPix2Pix to give user more control.
- **Text to Code/SQL**
- Use code understanding to convert use query to SQL or another executable programming language, including Domain Specific Languages
- Here is an example of the same: [qabot](github.com/hardbyte/qabot)
## Text
- **Autonomous Tagging of StackOverflow Questions**
- Make a multi-label classification system that automatically assigns tags for questions posted on a forum such as StackOverflow or Quora.
- Dataset: [StackLite](https://www.kaggle.com/stackoverflow/stacklite) or [10% sample](https://www.kaggle.com/stackoverflow/stacksample)
- **Keyword/Concept identification**
- Identify keywords from millions of questions
- Dataset: [StackOverflow question samples by Facebook](https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data)
- **Topic identification**
- Multi-label classification of printed media articles to topics
- Dataset: [Greek Media monitoring multi-label classification](https://www.kaggle.com/c/wise-2014/data)
### Natural Language Understanding
- **Sentence to Sentence semantic similarity**
- Can you identify question pairs that have the same intent or meaning?
- Dataset: [Quora question pairs](https://www.kaggle.com/c/quora-question-pairs/data) with similar questions marked
- **Fight online abuse**
- Can you confidently and accurately tell whether a particular comment is abusive?
- Dataset: [Toxic comments on Kaggle](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)
- **Open Domain question answering**
- Can you build a bot which answers questions according to the student's age or her curriculum?
- [Facebook's FAIR](https://github.com/facebookresearch/DrQA) is built in a similar way for Wikipedia.
- Dataset: [NCERT books](https://ncert.nic.in/textbook.php) for K-12/school students in India, [NarrativeQA by Google DeepMind](https://github.com/deepmind/narrativeqa) and [SQuAD by Stanford](https://rajpurkar.github.io/SQuAD-explorer/)
- **Automatic text summarization**
- Can you create a summary with the major points of the original document?
- Abstractive (write your own summary) and Extractive (select pieces of text from original) are two popular approaches
- Dataset: [CNN and DailyMail News Pieces](http://cs.nyu.edu/~kcho/DMQA/) by Google DeepMind
- **Copy-cat Bot**
- Generate plausible new text which looks like some other text
- Obama Speeches? For instance, you can create a bot which writes some [new speeches in Obama's style](https://medium.com/@samim/obama-rnn-machine-generated-political-speeches-c8abd18a2ea0)
- Trump Bot? Or a Twitter bot which mimics [@realDonaldTrump](http://www.twitter.com/@realdonaldtrump)
- Narendra Modi bot saying "*doston*"? Start by scrapping off his *Hindi* speeches from his [personal website](http://www.narendramodi.in)
- Example Dataset: [English Transcript of Modi speeches](https://github.com/mgupta1410/pm_modi_speeches_repo)
Check [mlm/blog](http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/) for some hints.
- **Sentiment Analysis**
- Do Twitter Sentiment Analysis on tweets sorted by geography and timestamp.
- Dataset: [Tweets sentiment tagged by humans](https://inclass.kaggle.com/c/si650winter11/data)
## Forecasting
- **Univariate Time Series Forecasting**
- How much will it rain this year?
- Dataset: [45 years of rainfall data](http://research.jisao.washington.edu/data_sets/widmann/)
- **Multi-variate Time Series Forecasting**
- How polluted will your town's air be? Pollution Level Forecasting
- Dataset: [Air Quality dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)
- **Demand/load forecasting**
- Find a short term forecast on electricity consumption of a single home
- Dataset: [Electricity consumption of a household](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)
- **Predict Blood Donation**
- We're interested in predicting if a blood donor will donate within a given time window.
- More on the problem statement at [Driven Data](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/page/7/).
- Dataset: [UCI ML Datasets Repo](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center)
## Recommendation systems
- **Movie Recommender**
- Can you predict the rating a user will give on a movie?
- Do this using the movies that user has rated in the past, as well as the ratings similar users have given similar movies.
- Dataset: [Netflix Prize](http://www.netflixprize.com/) and [MovieLens Datasets](https://grouplens.org/datasets/movielens/)
- **Search + Recommendation System**
- Predict which Xbox game a visitor will be most interested in based on their search query
- Dataset: [BestBuy](https://www.kaggle.com/c/acm-sf-chapter-hackathon-small/data)
- **Can you predict Influencers in the Social Network?**
- How can you predict social influencers?
- Dataset: [PeerIndex](https://www.kaggle.com/c/predict-who-is-more-influential-in-a-social-network/data)
## Vision
- **Image classification**
- Object recognition or image classification task is how Deep Learning shot up to it's present-day resurgence
- Datasets:
- [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html)
- [ImageNet](http://www.image-net.org/)
- [MS COCO](http://mscoco.org/) is the modern replacement to the ImageNet challenge
- [MNIST Handwritten Digit Classification Challenge](http://yann.lecun.com/exdb/mnist/) is the classic entry point
- [Character recognition (digits)](http://ai.stanford.edu/~btaskar/ocr/) is the good old Optical Character Recognition problem
- Bird Species Identification from an Image using the [Caltech-UCSD Birds dataset](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) dataset
- Diagnosing and Segmenting Brain Tumors and Phenotypes using MRI Scans
- Dataset: MICCAI Machine Learning Challenge aka [MLC 2014](https://www.nmr.mgh.harvard.edu/lab/laboratory-computational-imaging-biomarkers/miccai-2014-machine-learning-challenge)
- Identify endangered right whales in aerial photographs
- Dataset: [MOAA Right Whale](https://www.kaggle.com/c/noaa-right-whale-recognition)
- Can computer vision spot distracted drivers?
- Dataset: [State Farm Distracted Driver Detection](https://www.kaggle.com/c/state-farm-distracted-driver-detection/data) on Kaggle
- **Bone X-Ray competition**
- Can you identify if a hand is broken from a X-ray radiographs automatically with better than human performance?
- Stanford's Bone XRay Deep Learning Competition with [MURA Dataset](https://stanfordmlgroup.github.io/competitions/mura/)
- **Image Captioning**
- Can you caption/explain the photo a way human would?
- Dataset: [MS COCO](http://mscoco.org/dataset/#captions-challenge2015)
- **Image Segmentation/Object Detection**
- Can you extract an object of interest from an image?
- Dataset: [MS COCO](http://mscoco.org/dataset/#detections-challenge2017), [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge/data) on Kaggle
- **Large-Scale Video Understanding**
- Can you produce the best video tag predictions?
- Dataset: [YouTube 8M](https://research.google.com/youtube8m/index.html)
- **Video Summarization**
- Can you select the semantically relevant/important parts from the video?
- Example: [Fast-Forward Video Based on Semantic Extraction](https://arxiv.org/abs/1708.04160)
- Dataset: Unaware of any standard dataset or agreed upon metrics? I think [YouTube 8M](https://research.google.com/youtube8m/index.html) might be good starting point.
- **Style Transfer**
- Can you recompose images in the style of other images?
- Dataset: [fzliu on GitHub](https://github.com/fzliu/style-transfer/tree/master/images) shared target and source images with results
- **Chest XRay**
- Can you detect if someone is sick from their chest XRay? Or guess their radiology report?
- Dataset: [MIMIC-CXR at Physionet](https://physionet.org/content/mimic-cxr/2.0.0/)
- **Clinical Diagnostics: Image Identification, classification & segmentation**
- Can you help build an open source software for lung cancer detection to help radiologists?
- Link: [Concept to clinic](https://concepttoclinic.drivendata.org/) challenge on DrivenData
- **Satellite Imagery Processing for Socioeconomic Analysis**
- Can you estimate the standard of living or energy consumption of a place from night time satellite imagery?
- Reference for Project details: [Stanford Poverty Estimation Project](http://sustain.stanford.edu/predicting-poverty/)
- **Satellite Imagery Processing for Automated Tagging**
- Can you automatically tag satellite images with human features such as buildings, roads, waterways and so on?
- Help free the manual effort in tagging satellite imagery: [Kaggle Dataset by DSTL, UK](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection)
## Music
- **Music/Audio Recommendation Systems**
- Can you tell if two songs are similar using their sound or lyrics?
- Dataset: [Million Songs Dataset](https://labrosa.ee.columbia.edu/millionsong/) and it's 1% sample.
- Example: [Anusha et al](https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf)
- **Music Genre recognition using neural networks**
- Can you identify the musical genre using their spectrograms or other sound information?
- Datasets: [FMA](https://github.com/mdeff/fma) or [GTZAN on Keras](https://github.com/Hguimaraes/gtzan.keras)
- Get started with [Librosa](https://librosa.github.io/librosa/index.html) for feature extraction
---
### FAQ
- **Can I use the ideas here for my thesis?**
Yes, totally! I'd love to know how it went.
- **Do you have any advice before I start my project?**
[Advice for Short Term Machine Learning Projects](https://rockt.github.io/2018/08/29/msc-advice) by Tim R. is a pretty good starting point!
- **How can I add my ideas here?**
Just send a pull request and we'll discuss?
- **Hey, something is wrong here!**
Yikes, I am sorry. Please tell me by raising a [GitHub issue](https://github.com/NirantK/awesome-project-ideas/issues).
I'll fix it as soon as possible.
### Acknowledgements
Problems are motivated by the ones shared at:
- [CMU Machine Learning](http://www.cs.cmu.edu/~./10701/projects.html)
- [Stanford CS229 Machine Learning Projects](http://cs229.stanford.edu/)
- [swyx](https://github.com/sw-yx/ai-notes/blob/main/Resources/AI-hackathon-stack.md)
### Credit
Built with lots of keyboard smashing and copy-pasta love by NirantK. Find me on [Twitter](http://www.twitter.com/@nirantk)!
### License
This repository is licensed under the MIT License. Please see the [LICENSE file](./LICENSE) for more details.
================================================
FILE: _config.yml
================================================
theme: jekyll-theme-minimal
gitextract_p5v86si0/ ├── .gitignore ├── LICENSE ├── PULL_REQUEST_TEMPLATE.md ├── README.md └── _config.yml
Condensed preview — 5 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (18K chars).
[
{
"path": ".gitignore",
"chars": 1157,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 1077,
"preview": "MIT License\n\nCopyright (c) 2017-2019 Nirant Kasliwal\n\nPermission is hereby granted, free of charge, to any person obtain"
},
{
"path": "PULL_REQUEST_TEMPLATE.md",
"chars": 495,
"preview": "DELETE EVERYTHING FROM THIS LINE AND BELOW\n\nYour idea should be in this template: \n\n**Main Idea Title**\n- Ask a question"
},
{
"path": "README.md",
"chars": 14540,
"preview": "<!-- markdownlint-disable MD033 -->\n\n# Awesome Deep Learning Project Ideas\n\n[![Awesome](https://cdn.rawgit.com/sindresor"
},
{
"path": "_config.yml",
"chars": 27,
"preview": "theme: jekyll-theme-minimal"
}
]
About this extraction
This page contains the full source code of the NirantK/awesome-project-ideas GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 5 files (16.9 KB), approximately 4.5k tokens. 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.