[
{
"path": ".gitattributes",
"content": "\ndocs/* linguist-documentation\n_img/* linguist-vendored\n\n"
},
{
"path": ".gitignore",
"content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# Numerous always-ignore extensions\n*.pyc\n*.diff\n*.err\n*.orig\n*.log\n*.rej\n*.swo\n*.swp\n*.vi\n*.cache\n*.egg-info\n*~\n*#\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\ndocs/_static/\ndocs/_templates/\n\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n\n"
},
{
"path": ".travis.yml",
"content": "language: python\npython:\n - \"2.7\"\n - \"3.4\"\n - \"3.5\"\n - \"3.6\"\n\n# command to install dependencies\ninstall:\n - pip install -r requirements.txt\n - pip install coveralls flake8\n\nbefore_script:\n # stop the build if there are Python syntax errors or undefined names\n - flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics\n # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide\n - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics\n\nscript:\n - coverage run --omit=*.virtualenvs*,*virtualenv* docs/source/conf.py test\n\n\nafter_success:\n coveralls\n\nsudo: enabled\ndist: trusty\n"
},
{
"path": "CODE_OF_CONDUCT.rst",
"content": "Contributor Covenant Code of Conduct\n====================================\n\nOur Pledge\n----------\n\nIn the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.\n\nOur Standards\n-------------\n\nExamples of behavior that contributes to creating a positive environment include:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a professional setting\n\nOur Responsibilities\n--------------------\n\nProject maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.\n\nScope\n-----\n\nThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\nEnforcement\n-----------\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at amirsina.torfi@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.\n\nAttribution\n------------\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/\n"
},
{
"path": "CONTRIBUTING.rst",
"content": "\n*************\nContributing\n*************\n\n*For typos, please do not create a pull request. Instead, declare them in issues or email the repository owner*. Please note we have a code of conduct, please follow it in all your interactions with the project.\n\n====================\nPull Request Process\n====================\n\nPlease consider the following criterions in order to help us in a better way:\n\n1. The pull request is mainly expected to be a link suggestion.\n2. Please make sure your suggested resources are not obsolete or broken.\n3. Ensure any install or build dependencies are removed before the end of the layer when doing a\n build and creating a pull request.\n4. Add comments with details of changes to the interface, this includes new environment\n variables, exposed ports, useful file locations and container parameters.\n5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you\n do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.\n\n============\nFinal Note\n============\n\nWe are looking forward to your kind feedback. Please help us to improve this open source project and make our work better.\nFor contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate\nyour kind feedback and elaborate code inspections.\n"
},
{
"path": "LICENSE",
"content": "LICENSE\n========\n\nMIT License\n\nCopyright (c) 2017 Amirsina Torfi\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "README.rst",
"content": ".. figure:: _img/mainpage/subscribe.gif\n :target: https://machinelearningmindset.com/subscription/\n\n********************************************\nTensorFlow-World-Resources - `Project Page`_\n********************************************\n.. _Project Page: http://tensorflow-world-resources.readthedocs.io/en/latest/\n\n.. .. image:: https://travis-ci.org/astorfi/TensorFlow-World-Resources.svg?branch=master\n.. :target: https://travis-ci.org/astorfi/TensorFlow-World-Resources\n.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\n :target: https://github.com/astorfi/TensorFlow-World-Resources/pulls\n.. image:: https://badges.frapsoft.com/os/v2/open-source.svg?v=102\n :target: https://github.com/ellerbrock/open-source-badge/\n.. image:: https://coveralls.io/repos/github/astorfi/TensorFlow-World-Resources/badge.svg?branch=master\n :target: https://coveralls.io/github/astorfi/TensorFlow-World-Resources?branch=master\n.. image:: https://img.shields.io/twitter/follow/amirsinatorfi.svg?label=Follow&style=social\n :target: https://twitter.com/amirsinatorfi\n\n.. image:: _img/mainpage/TensorFlow_World.gif\n\n#################\nTable of Contents\n#################\n.. contents::\n :local:\n :depth: 3\n\n============\nIntroduction\n============\n\nThe purpose of this project is to introduce a shortcut to developers and researcher\nfor finding useful resources about TensorFlow.\n\n\n\n-----------\nMotivation\n-----------\n\nThere are different motivations for this open source project.\n\n~~~~~~~~~~~~~~~~~~~~~\nWhy using TensorFlow?\n~~~~~~~~~~~~~~~~~~~~~\n\nA deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms\nand designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of\nTensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of\nthe pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs\nsuch as `Keras `_ and `Slim `_\nwhich gather lots of the design puzzle pieces. The interesting point about TensorFlow is that **its trace can be found anywhere these days**.\nLots of the researchers and developers are using it and *its community is growing with the speed of light*! So the possible issues can\nbe overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community.\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nWhat's the point of this open source project?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThere other similar repositories similar to this repository and are very\ncomprehensive and useful and to be honest they made me ponder if there is\na necessity for this repository! A great example is `awesome-tensorflow `_\nrepository which is a curated list of different TensorFlow resources.\n\n**The point of this repository is that the resources are being targeted**. The organization\nof the resources is such that the user can easily find the things he/she is looking for.\nWe divided the resources to a large number of categories that in the beginning one may\nhave a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources.\nEven if someone doesn't know what to look for, in the beginning, the general resources have\nbeen provided.\n\n\n------------------------------------\nHow to make the most of this effort\n------------------------------------\n\nThe written and visual resources have been split. Moreover, As one can search\nin the documentation, the number of categories might look to be too much. For\nfinding the most relevant resources, please at first look through the general resources.\n\n============================\nEntrance to TensorFlow World\n============================\n\nIn this section, different TensorFlow topics and their associated\nresources will be addressed.\n\n-------------\nInstallation\n-------------\n\n.. image:: _img/mainpage/installation.gif\n\nFirst of all, the TensorFlow must be installed!\n\n\n* `Installing TensorFlow`_: Official TensorFLow installation\n* `Install TensorFlow from the source`_: A comprehensive guide on how to install TensorFlow from the source using python/anaconda\n* `TensorFlow Installation`_: A short TensorFlow installation guide powered by NVIDIA\n* `7 SIMPLE STEPS TO INSTALL TENSORFLOW ON WINDOWS`_: A concise tutorial for installing TensorFlow on Windows\n\n.. _Installing TensorFlow: https://www.tensorflow.org/install/\n.. _Install TensorFlow from the source: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/installation\n.. _TensorFlow Installation: http://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-installation.html\n.. _7 SIMPLE STEPS TO INSTALL TENSORFLOW ON WINDOWS: http://saintlad.com/install-tensorflow-on-windows/\n\n\n* `Install TensorFlow on Ubuntu`_: A comprehensive tutorial on how to install TensorFlow on Ubuntu\n* `Installation of TensorFlow`_: The video covers how to setup TensorFlow\n* `Installing CPU and GPU TensorFlow on Windows`_: A tutorial on TensorFlow installation for Windows\n* `Installing the GPU version of TensorFlow for making use of your CUDA GPU`_: A GPU-targeted TensoFlow installation\n\n\n.. _Install TensorFlow on Ubuntu: https://www.youtube.com/watch?v=_3JFEPk4qQY&t=3s\n.. _Installation of TensorFlow: https://www.youtube.com/watch?v=CvspEt8kSIg\n.. _Installing CPU and GPU TensorFlow on Windows: https://www.youtube.com/watch?v=r7-WPbx8VuY\n.. _Installing the GPU version of TensorFlow for making use of your CUDA GPU: https://www.youtube.com/watch?v=io6Ajf5XkaM\n\n---------------\nGetting Started\n---------------\n\n.. image:: _img/mainpage/gettingstarted.gif\n\nThis part points to resources on how to start to code with TensorFLow\n\n\n* `Getting Started With TensorFlow Framework`_: This guide gets you started programming in TensorFlow\n* `learning TensorFlow Deep Learning`_:A great resource to start\n* `Welcome to TensorFlow World`_: A simple and concise start to TensorFLow\n\n.. _learning TensorFlow Deep Learning: http://learningtensorflow.com/getting_started/\n.. _Getting Started With TensorFlow Framework: https://www.tensorflow.org/get_started/get_started\n.. _Welcome to TensorFlow World: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/0-welcome\n\n\n* `Gentlest Introduction to Tensorflow `_\n* `TensorFlow in 5 Minutes `_\n* `Deep Learning with TensorFlow - Introduction to TensorFlow `_\n* `TensorFlow Tutorial (Sherry Moore, Google Brain) `_\n* `Deep Learning with Neural Networks and TensorFlow Introduction `_\n* `A fast with TensorFlow `_\n\n--------------------------\nGoing Deeper in TensorFLow\n--------------------------\n\n.. image:: _img/mainpage/goingdeep.gif\n\nAdvanced machine learning users can go deeper in TensorFlow in order to\n*hit the root*. Scratching the surface may never take us too further!\n\n\n* `TensorFlow Mechanics`_: More experienced machine learning users can dig more in TensorFlow\n* `Advanced TensorFlow`_: Advanced Tutorials in TensorFlow\n* `We Need to Go Deeper`_: A Practical Guide to Tensorflow and Inception\n* `Wide and Deep Learning - Better Together with TensorFlow`_: A tutorial by Google Research Blog\n\n.. _TensorFlow Mechanics: https://www.tensorflow.org/get_started/mnist/mechanics\n.. _Advanced TensorFlow: https://github.com/sjchoi86/advanced-tensorflow\n.. _We Need to Go Deeper: https://medium.com/initialized-capital/we-need-to-go-deeper-a-practical-guide-to-tensorflow-and-inception-50e66281804f\n.. _Wide and Deep Learning - Better Together with TensorFlow: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html\n\n\n* `TensorFlow DeepDive`_: More experienced machine learning users can dig more in TensorFlow\n* `Go Deeper - Transfer Learning`_: TensorFlow and Deep Learning\n* `Distributed TensorFlow - Design Patterns and Best Practices`_: A talk that was given at the Advanced Spark and TensorFlow Meetup\n* `Distributed TensorFlow Guide`_\n* `Fundamentals of TensorFlow`_\n* `TensorFlow Wide and Deep - Advanced Classification the easy way`_\n* `Tensorflow and deep learning - without a PhD`_: A great tutorial on TensoFLow workflow\n\n\n\n.. _TensorFlow DeepDive: https://www.youtube.com/watch?v=T0H6zF3K1mc\n.. _Go Deeper - Transfer Learning: https://www.youtube.com/watch?v=iu3MOQ-Z3b4\n.. _Distributed TensorFlow - Design Patterns and Best Practices: https://www.youtube.com/watch?v=YAkdydqUE2c\n.. _Distributed TensorFlow Guide: https://github.com/tmulc18/Distributed-TensorFlow-Guide\n.. _Fundamentals of TensorFlow: https://www.youtube.com/watch?v=EM6SU8QVSlY\n.. _TensorFlow Wide and Deep - Advanced Classification the easy way: https://www.youtube.com/watch?v=WKgNNC0VLhM\n.. _Tensorflow and deep learning - without a PhD: https://www.youtube.com/watch?v=vq2nnJ4g6N0\n\n\n============================\nProgramming with TensorFlow\n============================\n\nThe references here, deal with the details of programming and writing TensorFlow code.\n\n--------------------------------\nReading data and input pipeline\n--------------------------------\n\n.. image:: _img/mainpage/readingdata.gif\n\nThe first part is always how to prepare data and how to provide the pipeline to feed it to TensorFlow.\nUsually providing the input pipeline can be complicated, even more than the structure design!\n\n* `Dataset API for TensorFlow Input Pipelines`_: A TensorFlow official documentation on *Using the Dataset API for TensorFlow Input Pipelines*\n* `TesnowFlow input pipeline`_: Input pipeline provided by Stanford.\n* `TensorFlow input pipeline example`_: A working example.\n* `TensorFlow Data Input`_: TensorFlow Data Input: Placeholders, Protobufs & Queues\n* `Reading data`_: The official documentation by the TensorFLow on how to read data\n* `basics of reading a CSV file`_: A tutorial on reading a CSV file\n* `Custom Data Readers`_: Official documentation on this how to define a reader.\n\n.. _Dataset API for TensorFlow Input Pipelines: https://github.com/tensorflow/tensorflow/tree/v1.2.0-rc1/tensorflow/contrib/data\n.. _TesnowFlow input pipeline: http://web.stanford.edu/class/cs20si/lectures/slides_09.pdf\n.. _TensorFlow input pipeline example: http://ischlag.github.io/2016/06/19/tensorflow-input-pipeline-example/\n.. _TensorFlow Data Input: https://indico.io/blog/tensorflow-data-inputs-part1-placeholders-protobufs-queues/\n.. _Reading data: https://www.tensorflow.org/programmers_guide/reading_data\n.. _basics of reading a CSV file: http://learningtensorflow.com/ReadingFilesBasic/\n.. _Custom Data Readers: https://www.tensorflow.org/extend/new_data_formats\n\n* `Tensorflow tutorial on TFRecords`_: A tutorial on how to transform data into TFRecords\n\n.. _Tensorflow tutorial on TFRecords: https://www.youtube.com/watch?v=F503abjanHA\n\n* `An introduction to TensorFlow queuing and threading`_: A tutorial on how to understand and create queues an efficient pipelines\n\n.. _An introduction to TensorFlow queuing and threading: http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/\n\n----------\nVariables\n----------\n\n.. image:: _img/mainpage/variables.gif\n\nVariables are supposed to hold the parameters and supersede by new values as the parameters are updated.\nVariables must be clearly set and initialized.\n\n\n~~~~~~~~~~~~~~~~~~~~~~~~\nCreation, Initialization\n~~~~~~~~~~~~~~~~~~~~~~~~\n\n* `Variables Creation and Initialization`_: An official documentation on setting up variables\n* `Introduction to TensorFlow Variables - Creation and Initialization`_: This tutorial deals with defining and initializing TensorFlow variables\n* `Variables`_: An introduction to variables\n\n.. _Variables Creation and Initialization: https://www.tensorflow.org/programmers_guide/variables\n.. _Introduction to TensorFlow Variables - Creation and Initialization: http://machinelearninguru.com/deep_learning/tensorflow/basics/variables/variables.html\n.. _Variables: http://learningtensorflow.com/lesson2/\n\n~~~~~~~~~~~~~~~~~~~~~~\nSaving and restoring\n~~~~~~~~~~~~~~~~~~~~~~\n\n* `Saving and Loading Variables`_: The official documentation on saving and restoring variables\n* `save and restore Tensorflow models`_: A quick tutorial to save and restore Tensorflow models\n\n.. _Saving and Loading Variables: https://www.tensorflow.org/programmers_guide/variables\n.. _save and restore Tensorflow models: http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/\n\n~~~~~~~~~~~~~~~~~\nSharing Variables\n~~~~~~~~~~~~~~~~~\n\n* `Sharing Variables`_: The official documentation on how to share variables\n\n.. _Sharing Variables: https://www.tensorflow.org/programmers_guide/variable_scope\n\n* `Deep Learning with Tensorflow - Tensors and Variables`_: A Tensorflow tutorial for introducing Tensors, Variables and Placeholders\n* `Tensorflow Variables`_: A quick introduction to TensorFlow variables\n* `Save and Restore in TensorFlow`_: TensorFlow Tutorial on Save and Restore variables\n\n.. _Deep Learning with Tensorflow - Tensors and Variables: https://www.youtube.com/watch?v=zgV-WzLyrYE\n.. _Tensorflow Variables: https://www.youtube.com/watch?v=UYyqNH3r4lk\n.. _Save and Restore in TensorFlow: https://www.tensorflow.org/programmers_guide/variable_scope\n\n--------------------\nTensorFlow Utilities\n--------------------\n\n.. image:: _img/mainpage/utility.png\n\n.. .. figure:: _img/mainpage/utility.png\n.. :scale: 20 %\n.. :alt: map to buried treasure\n..\n.. .. raw:: html\n..\n.. \n.. \n.. ![](\"https://github.com/astorfi/TensorFlow-World-Resources/blob/master/_img/mainpage/utility.png\"/)
\n.. \n..
\n\nDifferent utilities empower TensorFlow for faster computation in a more monitored manner.\n\n\n~~~~~~~~~~\nSupervisor\n~~~~~~~~~~\n\n* `Supervisor - Training Helper for Days-Long Trainings`_: The official documentation for TensorFLow Supervisor.\n* `Using TensorFlow Supervisor with TensorBoard summary groups`_: Using both TensorBoard and the Supervisor for profit\n* `Tensorflow example`_: A TensorFlow example using Supervisor.\n\n\n.. _Supervisor - Training Helper for Days-Long Trainings: https://www.tensorflow.org/programmers_guide/supervisor\n.. _Using TensorFlow Supervisor with TensorBoard summary groups: https://dev.widemeadows.de/2017/01/21/using-tensorflows-supervisor-with-tensorboard-summary-groups/\n.. _Tensorflow example: http://codata.colorado.edu/notebooks/tutorials/tensorflow_example_davis_yoshida/\n\n~~~~~~~~~~~~~~~~~~~\nTensorFlow Debugger\n~~~~~~~~~~~~~~~~~~~\n\n* `TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial`_: Official documentation for using debugger for MNIST\n* `How to Use TensorFlow Debugger with tf.contrib.learn`_: A more high-level method to use the debugger.\n* `Debugging TensorFlow Codes`_: A Practical Guide for Debugging TensorFlow Codes\n* `Debug TensorFlow Models with tfdbg`_: A tutorial by Google Developers Blog\n\n\n.. _TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial: https://www.tensorflow.org/programmers_guide/debugger\n.. _How to Use TensorFlow Debugger with tf.contrib.learn: https://www.tensorflow.org/programmers_guide/tfdbg-tflearn\n.. _Debugging TensorFlow Codes: https://github.com/wookayin/tensorflow-talk-debugging\n.. _Debug TensorFlow Models with tfdbg: https://developers.googleblog.com/2017/02/debug-tensorflow-models-with-tfdbg.html\n\n~~~~~~~~~~\nMetaGraphs\n~~~~~~~~~~\n\n* `Exporting and Importing a MetaGraph`_: Official TensorFlow documentation\n* `Model checkpointing using meta-graphs in TensorFlow`_: A working example\n\n.. _Exporting and Importing a MetaGraph: https://www.tensorflow.org/programmers_guide/meta_graph\n.. _Model checkpointing using meta-graphs in TensorFlow: http://www.seaandsailor.com/tensorflow-checkpointing.html\n\n~~~~~~~~~~~\nTensorboard\n~~~~~~~~~~~\n\n* `TensorBoard - Visualizing Learning`_: Official documentation by TensorFlow.\n* `TensorFlow Ops`_: Provided by Stanford\n* `Visualisation with TensorBoard`_: A tutorial on how to create and visualize a graph using TensorBoard\n* `Tensorboard`_: A brief tutorial on Tensorboard\n\n.. _TensorBoard - Visualizing Learning: https://www.tensorflow.org/get_started/summaries_and_tensorboard\n.. _TensorFlow Ops: http://web.stanford.edu/class/cs20si/lectures/notes_02.pdf\n.. _Visualisation with TensorBoard: http://learningtensorflow.com/Visualisation/\n.. _Tensorboard: http://edwardlib.org/tutorials/tensorboard\n\n\n* `Hands-on TensorBoard (TensorFlow Dev Summit 2017)`_: An introduction to the amazing things you can do with TensorBoard\n* `Tensorboard Explained in 5 Min`_: Providing the code for a simple handwritten character classifier in Python and visualizing it in Tensorboard\n* `How to Use Tensorboard`_: Going through a bunch of different features in Tensorboard\n\n\n.. _Hands-on TensorBoard (TensorFlow Dev Summit 2017): https://www.youtube.com/watch?v=eBbEDRsCmv4\n.. _Tensorboard Explained in 5 Min: https://www.youtube.com/watch?v=3bownM3L5zM\n.. _How to Use Tensorboard: https://www.youtube.com/watch?v=fBVEXKp4DIc\n\n====================\nTensorFlow Tutorials\n====================\n\nThis section is dedicated to provide tutorial resources on the implementation of\ndifferent models with TensorFlow.\n\n------------------------------\nLinear and Logistic Regression\n------------------------------\n\n.. image:: _img/mainpage/logisticregression.png\n\n\n* `TensorFlow Linear Model Tutorial`_: Using TF.Learn API in TensorFlow to solve a binary classification problem\n* `Linear Regression in Tensorflow`_: Predicting house prices in Boston area\n* `Linear regression with Tensorflow`_: Make use of tensorflow for numeric computation using data flow graphs\n* `Logistic Regression in Tensorflow with SMOTE`_: Implementation of Logistic Regression in TensorFlow\n* `A TensorFlow Tutorial - Email Classification`_: Using a simple logistic regression classifier\n* `Linear Regression using TensorFlow`_: Training a linear model by TensorFlow\n* `Logistic Regression using TensorFlow`_: Training a logistic regression by TensorFlow for binary classification\n\n\n.. _TensorFlow Linear Model Tutorial: https://www.tensorflow.org/tutorials/wide\n.. _Linear Regression in Tensorflow: https://aqibsaeed.github.io/2016-07-07-TensorflowLR/\n.. _Linear regression with Tensorflow: https://www.linkedin.com/pulse/linear-regression-tensorflow-iv%C3%A1n-corrales-solera\n.. _Logistic Regression in Tensorflow with SMOTE: https://aqibsaeed.github.io/2016-08-10-logistic-regression-tf/\n.. _A TensorFlow Tutorial - Email Classification: http://jrmeyer.github.io/tutorial/2016/02/01/TensorFlow-Tutorial.html\n.. _Linear Regression using TensorFlow: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/2-basics_in_machine_learning/linear_regression\n.. _Logistic Regression using TensorFlow: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/2-basics_in_machine_learning/logistic_regression\n\n* `Deep Learning with Tensorflow - Logistic Regression`_: A tutorial on Logistic Regression\n* `Deep Learning with Tensorflow - Linear Regression with TensorFlow`_: A tutorial on Linear Regression\n\n.. _Deep Learning with Tensorflow - Logistic Regression: https://www.youtube.com/watch?v=4cBRxZavvTo&t=1s\n.. _Deep Learning with Tensorflow - Linear Regression with TensorFlow: https://www.youtube.com/watch?v=zNalsMIB3NE\n\n\n------------------------------\nConvolutional Neural Networks\n------------------------------\n\n.. image:: _img/mainpage/CNNs.png\n\n\n* `Convolutional Neural Networks`_: Official TensorFlow documentation\n* `Convolutional Neural Networks using TensorFlow`_: Training a classifier using convolutional neural networks\n* `Image classifier using convolutional neural network`_: Building a convolutional neural network based image classifier\n* `Convolutional Neural Network CNN with TensorFlow tutorial`_: It covers how to write a basic convolutional neural network within TensorFlow with Python\n* `Deep Learning CNNs in Tensorflow with GPUs`_: Designing the architecture of a convolutional neural network (CNN)\n\n\n.. _Convolutional Neural Networks: https://www.tensorflow.org/tutorials/deep_cnn\n.. _Convolutional Neural Networks using TensorFlow: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/3-neural_network/convolutiona_neural_network\n.. _Image classifier using convolutional neural network: http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/\n.. _Convolutional Neural Network CNN with TensorFlow tutorial: https://pythonprogramming.net/cnn-tensorflow-convolutional-nerual-network-machine-learning-tutorial/\n.. _Deep Learning CNNs in Tensorflow with GPUs: https://hackernoon.com/deep-learning-cnns-in-tensorflow-with-gpus-cba6efe0acc2\n\n* `Deep Learning with Neural Networks`_: Convolutional Neural Networks with TensorFlow\n* `TensorFlow Tutorial`_: Convolutional Neural Network\n* `Understanding Convolution with TensorFlow`_: A tutorial on Convolution operation with TensorFlow\n* `CNN - Deep Learning with Tensorflow`_: Convolutional Network with TensorFlow\n\n.. _Deep Learning with Neural Networks: https://www.youtube.com/watch?v=mynJtLhhcXk\n.. _TensorFlow Tutorial: https://www.youtube.com/watch?v=HMcx-zY8JSg\n.. _Understanding Convolution with TensorFlow: https://www.youtube.com/watch?v=ETdaP_bBNWc\n.. _CNN - Deep Learning with Tensorflow: https://www.youtube.com/watch?v=yL-MkBSv18c\n\n-------------------------\nRecurrent Neural Networks\n-------------------------\n\n.. image:: _img/mainpage/RNN.png\n\n\n\n* `Recurrent Neural Networks`_: TensorFlow official documentation\n* `How to build a Recurrent Neural Network in TensorFlow`_: How to build a simple working Recurrent Neural Network in TensorFlow\n* `Recurrent Neural Networks in Tensorflow`_: Building a vanilla recurrent neural network (RNN) from the ground up in Tensorflow\n* `RNNs in Tensorflow - a Practical Guide and Undocumented Features`_: Going over some of the best practices for working with RNNs in Tensorflow\n* `RNN / LSTM cell example in TensorFlow and Python`_: Covering how to code a Recurrent Neural Network model with an LSTM in TensorFlow\n* `Sequence prediction using recurrent neural networks(LSTM) with TensorFlow`_: How to approximate a sequence of vectors using a recurrent neural networks\n* `TensorFlow RNN Tutorial`_: Recurrent Neural Networks for exploring time series and developing speech recognition capabilities\n\n.. _Recurrent Neural Networks: https://www.tensorflow.org/tutorials/recurrent\n.. _How to build a Recurrent Neural Network in TensorFlow: https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767\n.. _Recurrent Neural Networks in Tensorflow: https://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html\n.. _RNNs in Tensorflow - a Practical Guide and Undocumented Features: http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/\n.. _RNN / LSTM cell example in TensorFlow and Python: https://pythonprogramming.net/rnn-tensorflow-python-machine-learning-tutorial/\n.. _Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html\n.. _TensorFlow RNN Tutorial: https://svds.com/tensorflow-rnn-tutorial/\n\n\n* `Deep Learning with Neural Networks and TensorFlow`_: Recurrent Neural Networks (RNN)\n* `An Introduction to LSTMs in Tensorflow`_: A brief tutorial\n* `Deep Learning with Tensorflow - The Recurrent Neural Network Model`_: A tutorial on the Recurrent Neural Network Models\n* `Sequence Models and the RNN API`_: TensorFlow Dev Summit 2017\n* `RNN Example in Tensorflow`_: A quick tutorial\n\n.. _Deep Learning with Neural Networks and TensorFlow: https://www.youtube.com/watch?v=hWgGJeAvLws\n.. _An Introduction to LSTMs in Tensorflow: https://www.youtube.com/watch?v=l4X-kZjl1gs\n.. _Deep Learning with Tensorflow - The Recurrent Neural Network Model: https://www.youtube.com/watch?v=C0xoB8L8ms0&t=89s\n.. _Sequence Models and the RNN API: https://www.youtube.com/watch?v=RIR_-Xlbp7s\n.. _RNN Example in Tensorflow: https://www.youtube.com/watch?v=dFARw8Pm0Gk\n\n-------------\nAutoencoders\n-------------\n\n.. image:: _img/mainpage/autoencoder.png\n\n* `Deep Autoencoder with TensorFlow`_: An open source project\n* `Variational Autoencoder in TensorFlow`_: A tutorial on Variational Autoencoder\n* `Diving Into TensorFlow With Stacked Autoencoders`_: A nice brief tutorials\n* `Convolutional Autoencoders in Tensorflow`_: Implementing a single layer CAE\n* `Variational Autoencoder using Tensorflow`_: Facial expression low dimensional embedding\n\n.. _Deep Autoencoder with TensorFlow: https://github.com/cmgreen210/TensorFlowDeepAutoencoder\n.. _Variational Autoencoder in TensorFlow: https://jmetzen.github.io/2015-11-27/vae.html\n.. _Diving Into TensorFlow With Stacked Autoencoders: http://cmgreen.io/2016/01/04/tensorflow_deep_autoencoder.html\n.. _Convolutional Autoencoders in Tensorflow: https://pgaleone.eu/neural-networks/deep-learning/2016/12/13/convolutional-autoencoders-in-tensorflow/\n.. _Variational Autoencoder using Tensorflow: http://int8.io/variational-autoencoder-in-tensorflow/\n\n\n* `Deep Learning with Tensorflow - Autoencoder Structure`_: Tutorial on Autoencoder models\n* `Deep Learning with Tensorflow - RBMs and Autoencoders`_: Tutorial on Restricted Boltzmann machines and AEs\n\n.. _Deep Learning with Tensorflow - Autoencoder Structure: https://www.youtube.com/watch?v=H_Bi_PQWJJc\n.. _Deep Learning with Tensorflow - RBMs and Autoencoders: https://www.youtube.com/watch?v=FsAvo0E5Pmw\n\n-----------------\nGenerative models\n-----------------\n\n.. image:: _img/mainpage/generative_model.png\n\n\n\n* `Generative Adversarial Nets in TensorFlow`_: Implementing GAN using TensorFlow, with MNIST data\n* `Generative Adversarial Networks`_: A working example of Generative Adversarial Networks\n\n.. _Generative Adversarial Nets in TensorFlow: http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/\n.. _Generative Adversarial Networks: http://edwardlib.org/tutorials/gan\n\n* `TensorFlow Tutorial - Adversarial Examples`_: A tutorial on a working example for generative models\n\n.. _TensorFlow Tutorial - Adversarial Examples: link\n\n-------------\nMultiple GPUs\n-------------\n\n.. image:: _img/mainpage/multiple_gpu.png\n\n* `Using GPUs`_: Official TensorFlow documentation\n* `Deep Learning with Multiple GPUs on Rescale`_: TensorFlow Tutorial\n\n.. _Using GPUs: https://www.tensorflow.org/tutorials/using_gpu\n.. _Deep Learning with Multiple GPUs on Rescale: https://blog.rescale.com/deep-learning-with-multiple-gpus-on-rescale-tensorflow/\n\n===================\nTensorFlow Projects\n===================\n\nThis section is dedicated to provide resources that are mainly open source projects developed by TensorFlow.\nThose might be comprehensive tutorials on working example.\n\n-----------------------\nComprehensive Tutorials\n-----------------------\n\n.. image:: _img/mainpage/tutorial.png\n\n* `TensorFlow-World`_: Concise and ready-to-use TensorFlow tutorials with detailed documentation\n* `TensorFlow-Tutorials`_: Introduction to deep learning based on Google's TensorFlow framework\n* `TensorFlow Tutorials`_: Organized tutorials in TensorFlow\n* `TensorFlow-Examples`_: Providing working examples in TensorFlow\n* `Tensorflow Tutorials using Jupyter Notebook`_: TensorFlow tutorials written in Python plus Jupyter Notebook\n\n.. _TensorFlow-World: https://github.com/astorfi/TensorFlow-World\n.. _TensorFlow-Tutorials: https://github.com/nlintz/TensorFlow-Tutorials\n.. _TensorFlow Tutorials: https://github.com/Hvass-Labs/TensorFlow-Tutorials\n.. _TensorFlow-Examples: https://github.com/aymericdamien/TensorFlow-Examples\n.. _Tensorflow Tutorials using Jupyter Notebook: https://github.com/sjchoi86/Tensorflow-101\n\n------\nModels\n------\n\n.. image:: _img/mainpage/models.png\n\n* `TensorFlow Models`_: Machine learning models implemented in TensorFlow\n* `Tensorflow VGG16 and VGG19`_: Implementation of VGG 16 and VGG 19 based on tensorflow-vgg16 and Caffe to Tensorflow\n* `ResNet in TensorFlow`_: Implementation of `Deep Residual Learning for Image Recognition `_\n* `Inception in TensorFlow`_: Train the Inception v3 architecture\n* `A TensorFlow implementation of DeepMind WaveNet paper`_: TensorFlow implementation of the `WaveNet generative neural network architecture `_ for audio generation\n* `3D Convolutional Neural Networks for Speaker Verification`_: Implementation of `3D Convolutional Neural Networks for Speaker Verification application `_ in TensorFlow.\n* `Domain Transfer Network (DTN)`_: The implementation of `Unsupervised Cross-Domain Image Generation `_ in TensorFlow\n* `Neural Style`_: The Neural Style algorithm implementation that synthesizes a pastiche\n* `SqueezeNet in TensorFlow`_: Tensorflow implementation of SqueezeNet\n\n\n.. _TensorFlow Models: https://github.com/tensorflow/models\n.. _Tensorflow VGG16 and VGG19: https://github.com/machrisaa/tensorflow-vgg\n.. _ResNet in TensorFlow: https://github.com/ry/tensorflow-resnet\n.. _Inception in TensorFlow: https://github.com/tensorflow/models/tree/master/inception\n.. _A TensorFlow implementation of DeepMind WaveNet paper: https://github.com/ibab/tensorflow-wavenet\n.. _3D Convolutional Neural Networks for Speaker Verification: https://github.com/astorfi/3D-convolutional-speaker-recognition\n.. _Domain Transfer Network (DTN): https://github.com/yunjey/domain-transfer-network\n.. _Neural Style: https://github.com/cysmith/neural-style-tf\n.. _SqueezeNet in TensorFlow: https://github.com/vonclites/squeezenet\n\n\n===================\nPublished Resources\n===================\n\nThis section is dedicated to provide published resources on TensorFlow, Such as websites, blogs, and books.\n\n\n\n---------------------------------\nOnline Courses and Documentations\n---------------------------------\n\n.. image:: _img/mainpage/online.png\n\n* `LearningTensorFlow`_: Beginner-level tutorials for a TensorFlow\n* `Deep Learning by Google`_: A free online course developed by Google and Udacity\n* `Tensorflow for Deep Learning Research`_: A comprehensive course by Stanford\n* `Creative Applications of Deep Learning with TensorFlow`_: A free online course on TensorFlow from Kadenze\n* `Deep Learning with TensorFlow Tutorial`_: In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow\n\n.. _LearningTensorFlow: https://learningtensorflow.com/\n.. _Deep Learning by Google: https://www.udacity.com/course/deep-learning--ud730\n.. _Tensorflow for Deep Learning Research: https://web.stanford.edu/class/cs20si/\n.. _Creative Applications of Deep Learning with TensorFlow: https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\n.. _Deep Learning with TensorFlow Tutorial: https://cognitiveclass.ai/courses/deep-learning-tensorflow/\n\n\n------\nBooks\n------\n\n.. image:: _img/mainpage/books.jpg\n\n* `TensorFlow Machine Learning Cookbook`_: Quick guide to implementing TensorFlow in your day-to-day machine learning activities\n* `Deep Learning with TensorFlow`_: Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems\n* `First contact with TensorFlow`_: An online book on TensorFlow\n* `Building Machine Learning Projects with TensorFlow`_: Learn how to implement TensorFlow in production\n* `Learning TensorFlow`_: This book is an end-to-end guide to TensorFlow\n* `Machine Learning with TensorFlow`_: Tackle common commercial machine learning problems with Google’s TensorFlow library\n* `Getting Started with TensorFlow`_: An easy-to-understand book on TensorFlow\n* `Hands-On Machine Learning with Scikit-Learn and TensorFlow`_: By using examples, theory, the book help to gain an understanding of the machine learning concepts\n* `Machine Learning with TensorFlow (MEAP)`_: An introduction to the concepts of TensorFlow\n\n.. _TensorFlow Machine Learning Cookbook: https://www.amazon.com/dp/B01HY3TC54/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1\n.. _Deep Learning with TensorFlow: https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-tensorflow\n.. _First contact with TensorFlow: http://jorditorres.org/first-contact-with-tensorflow/\n.. _Building Machine Learning Projects with TensorFlow: https://www.amazon.com/dp/B01M2Z8FS4/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1\n.. _Learning TensorFlow: http://shop.oreilly.com/product/0636920063698.do\n.. _Machine Learning with TensorFlow: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-tensorflow\n.. _Getting Started with TensorFlow: https://www.amazon.com/Getting-Started-TensorFlow-Giancarlo-Zaccone-ebook/dp/B01H1JD6JO\n.. _Hands-On Machine Learning with Scikit-Learn and TensorFlow: http://shop.oreilly.com/product/0636920052289.do\n.. _Machine Learning with TensorFlow (MEAP): https://www.manning.com/books/machine-learning-with-tensorflow\n\n\n============\nContributing\n============\n\n*For typos, please do not create a pull request. Instead, declare them in issues or email the repository owner*. Please note we have a code of conduct, please follow it in all your interactions with the project.\n\n--------------------\nPull Request Process\n--------------------\n\nPlease consider the following criterions in order to help us in a better way:\n\n1. The pull request is mainly expected to be a link suggestion.\n2. Please make sure your suggested resources are not obsolete or broken.\n3. Ensure any install or build dependencies are removed before the end of the layer when doing a\n build and creating a pull request.\n4. Add comments with details of changes to the interface, this includes new environment\n variables, exposed ports, useful file locations and container parameters.\n5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you\n do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.\n\n----------\nFinal Note\n----------\n\nWe are looking forward to your kind feedback. Please help us to improve this open source project and make our work better.\nFor contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate\nyour kind feedback and support.\n"
},
{
"path": "conf.py",
"content": "import os\nimport sys\n\nimport sphinx_rtd_theme\nsys.path.insert(0, os.path.abspath('_ext'))\n# from recommonmark.parser import CommonMarkParser\n\nsys.path.insert(0, os.path.abspath('..'))\nsys.path.append(os.path.dirname(__file__))\n# os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"readthedocs.settings.dev\")\n\n# from django.conf import settings\n\n# import django\n# django.setup()\n\n\nsys.path.append(os.path.abspath('_ext'))\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.intersphinx',\n]\ntemplates_path = ['_templates']\n\nedit_on_github_project = 'astorfi/TensorFlow-Roadmap'\nedit_on_github_branch = 'master'\n\nsource_suffix = ['.rst', '.md']\n# source_parsers = {\n# '.md': CommonMarkParser,\n# }\n\nmaster_doc = 'index'\nproject = u'TensorFlow-Roadmap'\ncopyright = u'2019, Amirsina Torfi'\nauthor = u'Amirsina Torfi'\nversion = '1.0'\nrelease = '1.0'\nexclude_patterns = ['_build']\ndefault_role = 'obj'\npygments_style = 'sphinx'\n# intersphinx_mapping = {\n# 'TensorFlow-World': ('https://github.com/astorfi/TensorFlow-World', None),\n# }\nhtmlhelp_basename = 'ReadTheDocsdoc'\nlatex_documents = [\n (master_doc, 'TensorFlow-Roadmap.tex', u'TensorFlow-Roadmap Documentation',\n u'Amirsina Torfi', 'manual'),\n]\nman_pages = [\n (master_doc, 'tensorFlow-roadmap', u'TensorFlow-Roadmap Documentation',\n [author], 1)\n]\nexclude_patterns = [\n # 'api' # needed for ``make gettext`` to not die.\n]\n\nlanguage = 'en'\n\nlocale_dirs = [\n 'locale/',\n]\ngettext_compact = False\n\nhtml_theme = 'sphinx_rtd_theme'\nhtml_static_path = ['_static']\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\nhtml_logo = '_img/tflogo.gif'\nhtml_theme_options = {\n 'logo_only': True,\n 'display_version': False,\n}\n\ngithub_url='https://github.com/astorfi/TensorFlow-Roadmap'\n\nhtml_context = {\n\"display_github\": True, # Add 'Edit on Github' link instead of 'View page source'\n\"last_updated\": True,\n\"commit\": False,\n 'github_url': 'https://github.com/astorfi/TensorFlow-Roadmap'\n}\n\n\n#def setup(app):\n# app.add_stylesheet('custom.css')\n"
},
{
"path": "docs/Makefile",
"content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = sphinx-build\nSPHINXPROJ = TensoFlow-World-Resources\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)"
},
{
"path": "docs/make.bat",
"content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=source\r\nset BUILDDIR=build\r\nset SPHINXPROJ=TensoFlow-World-Resources\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\r\n\r\n:end\r\npopd\r\n"
},
{
"path": "docs/source/_ext/djangodocs.py",
"content": "def setup(app):\n app.add_crossref_type(\n directivename=\"setting\",\n rolename=\"setting\",\n indextemplate=\"pair: %s; setting\",\n )\n"
},
{
"path": "docs/source/conf.py",
"content": "import os\nimport sys\n\nimport sphinx_rtd_theme\nsys.path.insert(0, os.path.abspath('_ext'))\n# from recommonmark.parser import CommonMarkParser\n\nsys.path.insert(0, os.path.abspath('..'))\nsys.path.append(os.path.dirname(__file__))\n# os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"readthedocs.settings.dev\")\n\n# from django.conf import settings\n\n# import django\n# django.setup()\n\n\nsys.path.append(os.path.abspath('_ext'))\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.intersphinx',\n]\ntemplates_path = ['_templates']\n\nedit_on_github_project = 'astorfi/TensorFlow-Roadmap'\nedit_on_github_branch = 'master'\n\nsource_suffix = ['.rst', '.md']\n# source_parsers = {\n# '.md': CommonMarkParser,\n# }\n\nmaster_doc = 'index'\nproject = u'TensorFlow-Roadmap'\ncopyright = u'2019, Amirsina Torfi'\nauthor = u'Amirsina Torfi'\nversion = '1.0'\nrelease = '1.0'\nexclude_patterns = ['_build']\ndefault_role = 'obj'\npygments_style = 'sphinx'\n# intersphinx_mapping = {\n# 'TensorFlow-World': ('https://github.com/astorfi/TensorFlow-World', None),\n# }\nhtmlhelp_basename = 'ReadTheDocsdoc'\nlatex_documents = [\n (master_doc, 'TensorFlow-Roadmap.tex', u'TensorFlow-Roadmap Documentation',\n u'Amirsina Torfi', 'manual'),\n]\nman_pages = [\n (master_doc, 'tensoflow-roadmap', u'TensorFlow-Roadmap Documentation',\n [author], 1)\n]\nexclude_patterns = [\n # 'api' # needed for ``make gettext`` to not die.\n]\n\nlanguage = 'en'\n\nlocale_dirs = [\n 'locale/',\n]\ngettext_compact = False\n\nhtml_theme = 'sphinx_rtd_theme'\nhtml_static_path = ['_static']\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\nhtml_logo = '_img/tflogo.gif'\nhtml_theme_options = {\n 'logo_only': True,\n 'display_version': False,\n}\n\ngithub_url='https://github.com/astorfi/TensorFlow-Roadmap'\n\nhtml_context = {\n\"display_github\": True, # Add 'Edit on Github' link instead of 'View page source'\n\"last_updated\": True,\n\"commit\": False,\n 'github_url': 'https://github.com/astorfi/TensorFlow-Roadmap'\n}\n\n\n#def setup(app):\n# app.add_stylesheet('custom.css')\n"
},
{
"path": "docs/source/content/books.rst",
"content": "===================\nPublished Resources\n===================\n\nThis section is dedicated to provide published resources on TensorFlow, Such as websites, blogs, and books.\n\n\n\n---------------------------------\nOnline Courses and Documentations\n---------------------------------\n\n* `LearningTensorFlow`_: Beginner-level tutorials for a TensorFlow\n* `Deep Learning by Google`_: A free online course developed by Google and Udacity\n* `Tensorflow for Deep Learning Research`_: A comprehensive course by Stanford\n* `Creative Applications of Deep Learning with TensorFlow`_: A non-free course on TensorFlow\n* `Deep Learning with TensorFlow Tutorial`_: In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow\n\n.. _LearningTensorFlow: https://learningtensorflow.com/\n.. _Deep Learning by Google: https://www.udacity.com/course/deep-learning--ud730\n.. _Tensorflow for Deep Learning Research: https://web.stanford.edu/class/cs20si/\n.. _Creative Applications of Deep Learning with TensorFlow: https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\n.. _Deep Learning with TensorFlow Tutorial: https://cognitiveclass.ai/courses/deep-learning-tensorflow/\n\n\n\n------\nBooks\n------\n\n* `TensorFlow Machine Learning Cookbook`_: Quick guide to implementing TensorFlow in your day-to-day machine learning activities\n* `Deep Learning with TensorFlow`_: Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems\n* `First contact with TensorFlow`_: An online book on TensorFlow\n* `Building Machine Learning Projects with TensorFlow`_: Learn how to implement TensorFlow in production\n* `Learning TensorFlow`_: This book is an end-to-end guide to TensorFlow\n* `Machine Learning with TensorFlow`_: Tackle common commercial machine learning problems with Google’s TensorFlow library\n* `Getting Started with TensorFlow`_: An easy-to-understand book on TensorFlow\n* `Hands-On Machine Learning with Scikit-Learn and TensorFlow`_: By using examples, theory, the book help to gain an understanding of the machine learning concepts\n* `Machine Learning with TensorFlow (MEAP)`_: An introduction to the concepts of TensorFlow\n\n.. _TensorFlow Machine Learning Cookbook: https://www.amazon.com/dp/B01HY3TC54/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1\n.. _Deep Learning with TensorFlow: https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-tensorflow\n.. _First contact with TensorFlow: http://jorditorres.org/first-contact-with-tensorflow/\n.. _Building Machine Learning Projects with TensorFlow: https://www.amazon.com/dp/B01M2Z8FS4/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1\n.. _Learning TensorFlow: http://shop.oreilly.com/product/0636920063698.do\n.. _Machine Learning with TensorFlow: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-tensorflow\n.. _Getting Started with TensorFlow: https://www.amazon.com/Getting-Started-TensorFlow-Giancarlo-Zaccone-ebook/dp/B01H1JD6JO\n.. _Hands-On Machine Learning with Scikit-Learn and TensorFlow: http://shop.oreilly.com/product/0636920052289.do\n.. _Machine Learning with TensorFlow (MEAP): https://www.manning.com/books/machine-learning-with-tensorflow\n"
},
{
"path": "docs/source/content/howtocode.rst",
"content": "============================\nProgramming with TensorFlow\n============================\n\n.. * `Title`_: some text\n.. .. _Title: link\n\nThe references here, deal with the details of programming and writing TensorFlow code.\n\n-------------------------------\nReading data and input pipeline\n-------------------------------\n\nThe first part is always how to prepare data and how to provide the pipeline to feed it to TensorFlow.\nUsually providing the input pipeline can be complicated, even more than the structure design!\n\n~~~~~~~~~~~~~~~~~\nWritten resources\n~~~~~~~~~~~~~~~~~\n\n* `Dataset API for TensorFlow Input Pipelines`_: A TensorFlow official documentation on *Using the Dataset API for TensorFlow Input Pipelines*\n* `TesnowFlow input pipeline`_: Input pipeline provided by Stanford.\n* `TensorFlow input pipeline example`_: A working example.\n* `TensorFlow Data Input`_: TensorFlow Data Input: Placeholders, Protobufs & Queues\n* `Reading data`_: The official documentation by the TensorFLow on how to read data\n* `basics of reading a CSV file`_: A tutorial on reading a CSV file\n* `Custom Data Readers`_: Official documentation on this how to define a reader.\n\n.. _Dataset API for TensorFlow Input Pipelines: https://github.com/tensorflow/tensorflow/tree/v1.2.0-rc1/tensorflow/contrib/data\n.. _TesnowFlow input pipeline: http://web.stanford.edu/class/cs20si/lectures/slides_09.pdf\n.. _TensorFlow input pipeline example: http://ischlag.github.io/2016/06/19/tensorflow-input-pipeline-example/\n.. _TensorFlow Data Input: https://indico.io/blog/tensorflow-data-inputs-part1-placeholders-protobufs-queues/\n.. _Reading data: https://www.tensorflow.org/programmers_guide/reading_data\n.. _basics of reading a CSV file: http://learningtensorflow.com/ReadingFilesBasic/\n.. _Custom Data Readers: https://www.tensorflow.org/extend/new_data_formats\n\n~~~~~~~~~~~~~~~~\nVisual resources\n~~~~~~~~~~~~~~~~\n\n* `Tensorflow tutorial on TFRecords`_: A tutorial on how to transform data into TFRecords\n\n.. _Tensorflow tutorial on TFRecords: https://www.youtube.com/watch?v=F503abjanHA\n\n----------\nVariables\n----------\n\nVariables are supposed to hold the parameters and supersede by new values as the parameters are updated.\nVariables must be clearly set and initialized.\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n^^^^^^^^^^^^^^^^^^^^^^^^\nCreation, Initialization\n^^^^^^^^^^^^^^^^^^^^^^^^\n\n* `Variables Creation and Initialization`_: An official documentation on setting up variables\n* `Introduction to TensorFlow Variables - Creation and Initialization`_: This tutorial deals with defining and initializing TensorFlow variables\n* `Variables`_: An introduction to variables\n\n.. _Variables Creation and Initialization: https://www.tensorflow.org/programmers_guide/variables\n.. _Introduction to TensorFlow Variables - Creation and Initialization: http://machinelearninguru.com/deep_learning/tensorflow/basics/variables/variables.html\n.. _Variables: http://learningtensorflow.com/lesson2/\n\n^^^^^^^^^^^^^^^^^^^^^\nSaving and restoring\n^^^^^^^^^^^^^^^^^^^^^\n\n* `Saving and Loading Variables`_: The official documentation on saving and restoring variables\n* `save and restore Tensorflow models`_: A quick tutorial to save and restore Tensorflow models\n\n.. _Saving and Loading Variables: https://www.tensorflow.org/programmers_guide/variables\n.. _save and restore Tensorflow models: http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/\n\n^^^^^^^^^^^^^^^^^\nSharing Variables\n^^^^^^^^^^^^^^^^^\n\n* `Sharing Variables`_: The official documentation on how to share variables\n\n.. _Sharing Variables: https://www.tensorflow.org/programmers_guide/variable_scope\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `Deep Learning with Tensorflow - Tensors and Variables`_: A Tensorflow tutorial for introducing Tensors, Variables and Placeholders\n* `Tensorflow Variables`_: A quick introduction to TensorFlow variables\n* `Save and Restore in TensorFlow`_: TensorFlow Tutorial on Save and Restore variables\n\n.. _Deep Learning with Tensorflow - Tensors and Variables: https://www.youtube.com/watch?v=zgV-WzLyrYE\n.. _Tensorflow Variables: https://www.youtube.com/watch?v=UYyqNH3r4lk\n.. _Save and Restore in TensorFlow: https://www.tensorflow.org/programmers_guide/variable_scope\n\n--------------------\nTensorFlow Utilities\n--------------------\n\nDifferent utilities empower TensorFlow for faster computation in a more monitored manner.\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n^^^^^^^^^^\nSupervisor\n^^^^^^^^^^\n\n* `Supervisor - Training Helper for Days-Long Trainings`_: The official documentation for TensorFLow Supervisor.\n* `Using TensorFlow Supervisor with TensorBoard summary groups`_: Using both TensorBoard and the Supervisor for profit\n* `Tensorflow example`_: A TensorFlow example using Supervisor.\n\n\n.. _Supervisor - Training Helper for Days-Long Trainings: https://www.tensorflow.org/programmers_guide/supervisor\n.. _Using TensorFlow Supervisor with TensorBoard summary groups: https://dev.widemeadows.de/2017/01/21/using-tensorflows-supervisor-with-tensorboard-summary-groups/\n.. _Tensorflow example: http://codata.colorado.edu/notebooks/tutorials/tensorflow_example_davis_yoshida/\n\n^^^^^^^^^^^^^^^^^^^\nTensorFlow Debugger\n^^^^^^^^^^^^^^^^^^^\n\n* `TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial`_: Official documentation for using debugger for MNIST\n* `How to Use TensorFlow Debugger with tf.contrib.learn`_: A more high-level method to use the debugger.\n* `Debugging TensorFlow Codes`_: A Practical Guide for Debugging TensorFlow Codes\n* `Debug TensorFlow Models with tfdbg`_: A tutorial by Google Developers Blog\n\n\n.. _TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial: https://www.tensorflow.org/programmers_guide/debugger\n.. _How to Use TensorFlow Debugger with tf.contrib.learn: https://www.tensorflow.org/programmers_guide/tfdbg-tflearn\n.. _Debugging TensorFlow Codes: https://github.com/wookayin/tensorflow-talk-debugging\n.. _Debug TensorFlow Models with tfdbg: https://developers.googleblog.com/2017/02/debug-tensorflow-models-with-tfdbg.html\n\n^^^^^^^^^^\nMetaGraphs\n^^^^^^^^^^\n\n* `Exporting and Importing a MetaGraph`_: Official TensorFlow documentation\n* `Model checkpointing using meta-graphs in TensorFlow`_: A working example\n\n.. _Exporting and Importing a MetaGraph: https://www.tensorflow.org/programmers_guide/meta_graph\n.. _Model checkpointing using meta-graphs in TensorFlow: http://www.seaandsailor.com/tensorflow-checkpointing.html\n\n^^^^^^^^^^^\nTensorboard\n^^^^^^^^^^^\n\n* `TensorBoard - Visualizing Learning`_: Official documentation by TensorFlow.\n* `TensorFlow Ops`_: Provided by Stanford\n* `Visualisation with TensorBoard`_: A tutorial on how to create and visualize a graph using TensorBoard\n* `Tensorboard`_: A brief tutorial on Tensorboard\n\n.. _TensorBoard - Visualizing Learning: https://www.tensorflow.org/get_started/summaries_and_tensorboard\n.. _TensorFlow Ops: http://web.stanford.edu/class/cs20si/lectures/notes_02.pdf\n.. _Visualisation with TensorBoard: http://learningtensorflow.com/Visualisation/\n.. _Tensorboard: http://edwardlib.org/tutorials/tensorboard\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `Hands-on TensorBoard (TensorFlow Dev Summit 2017)`_: An introduction to the amazing things you can do with TensorBoard\n* `Tensorboard Explained in 5 Min`_: Providing the code for a simple handwritten character classifier in Python and visualizing it in Tensorboard\n* `How to Use Tensorboard`_: Going through a bunch of different features in Tensorboard\n\n\n.. _Hands-on TensorBoard (TensorFlow Dev Summit 2017): https://www.youtube.com/watch?v=eBbEDRsCmv4\n.. _Tensorboard Explained in 5 Min: https://www.youtube.com/watch?v=3bownM3L5zM\n.. _How to Use Tensorboard: https://www.youtube.com/watch?v=fBVEXKp4DIc\n"
},
{
"path": "docs/source/content/projects.rst",
"content": "===================\nTensorFlow Projects\n===================\n\nThis section is dedicated to provide resources that are mainly open source projects developed by TensorFlow.\nThose might be comprehensive tutorials on working example.\n\n-----------------------\nComprehensive Tutorials\n-----------------------\n\n* `TensorFlow-World`_: Concise and ready-to-use TensorFlow tutorials with detailed documentation\n* `TensorFlow-Tutorials`_: Introduction to deep learning based on Google's TensorFlow framework\n* `TensorFlow Tutorials`_: Organized tutorials in TensorFlow\n* `TensorFlow-Examples`_: Providing working examples in TensorFlow\n* `Tensorflow Tutorials using Jupyter Notebook`_: TensorFlow tutorials written in Python plus Jupyter Notebook\n* `TensorFlow for Deep Learning Research`_: Code examples for the Stanford course CS 20SI\n\n.. _TensorFlow-World: https://github.com/astorfi/TensorFlow-World\n.. _TensorFlow-Tutorials: https://github.com/nlintz/TensorFlow-Tutorials\n.. _TensorFlow Tutorials: https://github.com/Hvass-Labs/TensorFlow-Tutorials\n.. _TensorFlow-Examples: https://github.com/aymericdamien/TensorFlow-Examples\n.. _Tensorflow Tutorials using Jupyter Notebook: https://github.com/sjchoi86/Tensorflow-101\n.. _TensorFlow for Deep Learning Research: https://github.com/chiphuyen/tf-stanford-tutorials\n\n------\nModels\n------\n\n* `TensorFlow Models`_: Machine learning models implemented in TensorFlow\n* `Tensorflow VGG16 and VGG19`_: Implementation of VGG 16 and VGG 19 based on tensorflow-vgg16 and Caffe to Tensorflow\n* `ResNet in TensorFlow`_: Implementation of `Deep Residual Learning for Image Recognition `_\n* `Inception in TensorFlow`_: Train the Inception v3 architecture\n* `A TensorFlow implementation of DeepMind WaveNet paper`_: TensorFlow implementation of the `WaveNet generative neural network architecture `_ for audio generation\n* `3D Convolutional Neural Networks for Speaker Verification`_: Implementation of `3D Convolutional Neural Networks for Speaker Verification application `_ in TensorFlow.\n* `Domain Transfer Network (DTN)`_: The implementation of `Unsupervised Cross-Domain Image Generation `_ in TensorFlow\n* `Neural Style`_: The Neural Style algorithm implementation that synthesizes a pastiche\n* `SqueezeNet in TensorFlow`_: Tensorflow implementation of SqueezeNet\n\n\n.. _TensorFlow Models: https://github.com/tensorflow/models\n.. _Tensorflow VGG16 and VGG19: https://github.com/machrisaa/tensorflow-vgg\n.. _ResNet in TensorFlow: https://github.com/ry/tensorflow-resnet\n.. _Inception in TensorFlow: https://github.com/tensorflow/models/tree/master/inception\n.. _A TensorFlow implementation of DeepMind WaveNet paper: https://github.com/ibab/tensorflow-wavenet\n.. _3D Convolutional Neural Networks for Speaker Verification: https://github.com/astorfi/3D-convolutional-speaker-recognition\n.. _Domain Transfer Network (DTN): https://github.com/yunjey/domain-transfer-network\n.. _Neural Style: https://github.com/cysmith/neural-style-tf\n.. _SqueezeNet in TensorFlow: https://github.com/vonclites/squeezenet\n"
},
{
"path": "docs/source/content/tutorials.rst",
"content": "====================\nTensorFlow Tutorials\n====================\n\nThis section is dedicated to provide tutorial resources on the implementation of\ndifferent models with TensorFlow.\n\n------------------------------\nLinear and Logistic Regression\n------------------------------\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n* `TensorFlow Linear Model Tutorial`_: Using TF.Learn API in TensorFlow to solve a binary classification problem\n* `Linear Regression in Tensorflow`_: Predicting house prices in Boston area\n* `Linear regression with Tensorflow`_: Make use of tensorflow for numeric computation using data flow graphs\n* `Logistic Regression in Tensorflow with SMOTE`_: Implementation of Logistic Regression in TensorFlow\n* `A TensorFlow Tutorial - Email Classification`_: Using a simple logistic regression classifier\n* `Linear Regression using TensorFlow`_: Training a linear model by TensorFlow\n* `Logistic Regression using TensorFlow`_: Training a logistic regression by TensorFlow for binary classification\n\n\n.. _TensorFlow Linear Model Tutorial: https://www.tensorflow.org/tutorials/wide\n.. _Linear Regression in Tensorflow: https://aqibsaeed.github.io/2016-07-07-TensorflowLR/\n.. _Linear regression with Tensorflow: https://www.linkedin.com/pulse/linear-regression-tensorflow-iv%C3%A1n-corrales-solera\n.. _Logistic Regression in Tensorflow with SMOTE: https://aqibsaeed.github.io/2016-08-10-logistic-regression-tf/\n.. _A TensorFlow Tutorial - Email Classification: http://jrmeyer.github.io/tutorial/2016/02/01/TensorFlow-Tutorial.html\n.. _Linear Regression using TensorFlow: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/2-basics_in_machine_learning/linear_regression\n.. _Logistic Regression using TensorFlow: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/2-basics_in_machine_learning/logistic_regression\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `Deep Learning with Tensorflow - Logistic Regression`_: A tutorial on Logistic Regression\n* `Deep Learning with Tensorflow - Linear Regression with TensorFlow`_: A tutorial on Linear Regression\n\n.. _Deep Learning with Tensorflow - Logistic Regression: https://www.youtube.com/watch?v=4cBRxZavvTo&t=1s\n.. _Deep Learning with Tensorflow - Linear Regression with TensorFlow: https://www.youtube.com/watch?v=zNalsMIB3NE\n\n\n------------------------------\nConvolutional Neural Networks\n------------------------------\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n* `Convolutional Neural Networks`_: Official TensorFlow documentation\n* `Convolutional Neural Networks using TensorFlow`_: Training a classifier using convolutional neural networks\n* `Image classifier using convolutional neural network`_: Building a convolutional neural network based image classifier\n* `Convolutional Neural Network CNN with TensorFlow tutorial`_: It covers how to write a basic convolutional neural network within TensorFlow with Python\n* `Deep Learning CNNs in Tensorflow with GPUs`_: Designing the architecture of a convolutional neural network (CNN)\n\n\n.. _Convolutional Neural Networks: https://www.tensorflow.org/tutorials/deep_cnn\n.. _Convolutional Neural Networks using TensorFlow: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/3-neural_network/convolutiona_neural_network\n.. _Image classifier using convolutional neural network: http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/\n.. _Convolutional Neural Network CNN with TensorFlow tutorial: https://pythonprogramming.net/cnn-tensorflow-convolutional-nerual-network-machine-learning-tutorial/\n.. _Deep Learning CNNs in Tensorflow with GPUs: https://hackernoon.com/deep-learning-cnns-in-tensorflow-with-gpus-cba6efe0acc2\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `Deep Learning with Neural Networks`_: Convolutional Neural Networks with TensorFlow\n* `TensorFlow Tutorial`_: Convolutional Neural Network\n* `Understanding Convolution with TensorFlow`_: A tutorial on Convolution operation with TensorFlow\n* `Deep Learning with Tensorflow`_: Convolutional Network with TensorFlow\n\n.. _Deep Learning with Neural Networks: https://www.youtube.com/watch?v=mynJtLhhcXk\n.. _TensorFlow Tutorial: https://www.youtube.com/watch?v=HMcx-zY8JSg\n.. _Understanding Convolution with TensorFlow: https://www.youtube.com/watch?v=ETdaP_bBNWc\n.. _Deep Learning with Tensorflow: https://www.youtube.com/watch?v=yL-MkBSv18c\n\n-------------------------\nRecurrent Neural Networks\n-------------------------\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n* `Recurrent Neural Networks`_: TensorFlow official documentation\n* `How to build a Recurrent Neural Network in TensorFlow`_: How to build a simple working Recurrent Neural Network in TensorFlow\n* `Recurrent Neural Networks in Tensorflow`_: Building a vanilla recurrent neural network (RNN) from the ground up in Tensorflow\n* `RNNs in Tensorflow - a Practical Guide and Undocumented Features`_: Going over some of the best practices for working with RNNs in Tensorflow\n* `RNN / LSTM cell example in TensorFlow and Python`_: Covering how to code a Recurrent Neural Network model with an LSTM in TensorFlow\n* `Sequence prediction using recurrent neural networks(LSTM) with TensorFlow`_: How to approximate a sequence of vectors using a recurrent neural networks\n* `TensorFlow RNN Tutorial`_: Recurrent Neural Networks for exploring time series and developing speech recognition capabilities\n\n.. _Recurrent Neural Networks: https://www.tensorflow.org/tutorials/recurrent\n.. _How to build a Recurrent Neural Network in TensorFlow: https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767\n.. _Recurrent Neural Networks in Tensorflow: https://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html\n.. _RNNs in Tensorflow - a Practical Guide and Undocumented Features: http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/\n.. _RNN / LSTM cell example in TensorFlow and Python: https://pythonprogramming.net/rnn-tensorflow-python-machine-learning-tutorial/\n.. _Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html\n.. _TensorFlow RNN Tutorial: https://svds.com/tensorflow-rnn-tutorial/\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `Deep Learning with Neural Networks and TensorFlow`_: Recurrent Neural Networks (RNN)\n* `An Introduction to LSTMs in Tensorflow`_: A brief tutorial\n* `Deep Learning with Tensorflow - The Recurrent Neural Network Model`_: A tutorial on the Recurrent Neural Network Models\n* `Sequence Models and the RNN API`_: TensorFlow Dev Summit 2017\n* `RNN Example in Tensorflow`_: A quick tutorial\n\n.. _Deep Learning with Neural Networks and TensorFlow: https://www.youtube.com/watch?v=hWgGJeAvLws\n.. _An Introduction to LSTMs in Tensorflow: https://www.youtube.com/watch?v=l4X-kZjl1gs\n.. _Deep Learning with Tensorflow - The Recurrent Neural Network Model: https://www.youtube.com/watch?v=C0xoB8L8ms0&t=89s\n.. _Sequence Models and the RNN API: https://www.youtube.com/watch?v=RIR_-Xlbp7s\n.. _RNN Example in Tensorflow: https://www.youtube.com/watch?v=dFARw8Pm0Gk\n\n-------------\nAutoencoders\n-------------\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n* `Deep Autoencoder with TensorFlow`_: An open source project\n* `Variational Autoencoder in TensorFlow`_: A tutorial on Variational Autoencoder\n* `Diving Into TensorFlow With Stacked Autoencoders`_: A nice brief tutorials\n* `Convolutional Autoencoders in Tensorflow`_: Implementing a single layer CAE\n* `Variational Autoencoder using Tensorflow`_: Facial expression low dimensional embedding\n\n.. _Deep Autoencoder with TensorFlow: https://github.com/cmgreen210/TensorFlowDeepAutoencoder\n.. _Variational Autoencoder in TensorFlow: https://jmetzen.github.io/2015-11-27/vae.html\n.. _Diving Into TensorFlow With Stacked Autoencoders: http://cmgreen.io/2016/01/04/tensorflow_deep_autoencoder.html\n.. _Convolutional Autoencoders in Tensorflow: https://pgaleone.eu/neural-networks/deep-learning/2016/12/13/convolutional-autoencoders-in-tensorflow/\n.. _Variational Autoencoder using Tensorflow: http://int8.io/variational-autoencoder-in-tensorflow/\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `Deep Learning with Tensorflow - Autoencoder Structure`_: Tutorial on Autoencoder models\n* `Deep Learning with Tensorflow - RBMs and Autoencoders`_: Tutorial on Restricted Boltzmann machines and AEs\n\n.. _Deep Learning with Tensorflow - Autoencoder Structure: https://www.youtube.com/watch?v=H_Bi_PQWJJc\n.. _Deep Learning with Tensorflow - RBMs and Autoencoders: https://www.youtube.com/watch?v=FsAvo0E5Pmw\n\n-----------------\nGenerative models\n-----------------\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n* `Generative Adversarial Nets in TensorFlow`_: Implementing GAN using TensorFlow, with MNIST data\n* `Generative Adversarial Networks`_: A working example of Generative Adversarial Networks\n\n.. _Generative Adversarial Nets in TensorFlow: http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/\n.. _Generative Adversarial Networks: http://edwardlib.org/tutorials/gan\n\n~~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~~\n\n* `TensorFlow Tutorial - Adversarial Examples`_: A tutorial on a working example for generative models\n\n.. _TensorFlow Tutorial - Adversarial Examples: link\n\n-------------\nMultiple GPUs\n-------------\n\n~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~\n\n* `Using GPUs`_: Official TensorFlow documentation\n* `Deep Learning with Multiple GPUs on Rescale`_: TensorFlow Tutorial\n\n.. _Using GPUs: https://www.tensorflow.org/tutorials/using_gpu\n.. _Deep Learning with Multiple GPUs on Rescale: https://blog.rescale.com/deep-learning-with-multiple-gpus-on-rescale-tensorflow/\n"
},
{
"path": "docs/source/content/warmup.rst",
"content": "============================\nEntrance to TensorFlow World\n============================\n\nIn this section, different TensorFlow topics and their associated\nresources will be addressed.\n\n-------------\nInstallation\n-------------\n\nFirst of all, the TensorFlow must be installed!\n\n~~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~~\n\n* `Installing TensorFlow`_: Official TensorFLow installation\n* `Install TensorFlow from the source`_: A comprehensive guide on how to install TensorFlow from the source using python/anaconda\n* `TensorFlow Installation`_: A short TensorFlow installation guide powered by NVIDIA\n* `7 SIMPLE STEPS TO INSTALL TENSORFLOW ON WINDOWS`_: A concise tutorial for installing TensorFlow on Windows\n\n.. _Installing TensorFlow: https://www.tensorflow.org/install/\n.. _Install TensorFlow from the source: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/installation\n.. _TensorFlow Installation: http://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-installation.html\n.. _7 SIMPLE STEPS TO INSTALL TENSORFLOW ON WINDOWS: http://saintlad.com/install-tensorflow-on-windows/\n\n\n~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~\n\n* `Install TensorFlow on Ubuntu`_: A comprehensive tutorial on how to install TensorFlow on Ubuntu\n* `Installation of TensorFlow`_: The video covers how to setup TensorFlow\n* `Installing CPU and GPU TensorFlow on Windows`_: A tutorial on TensorFlow installation for Windows\n* `Installing the GPU version of TensorFlow for making use of your CUDA GPU`_: A GPU-targeted TensoFlow installation\n\n\n.. _Install TensorFlow on Ubuntu: https://www.youtube.com/watch?v=_3JFEPk4qQY&t=3s\n.. _Installation of TensorFlow: https://www.youtube.com/watch?v=CvspEt8kSIg\n.. _Installing CPU and GPU TensorFlow on Windows: https://www.youtube.com/watch?v=r7-WPbx8VuY\n.. _Installing the GPU version of TensorFlow for making use of your CUDA GPU: https://www.youtube.com/watch?v=io6Ajf5XkaM\n\n---------------\nGetting Started\n---------------\n\nThis part points to resources on how to start to code with TensorFLow\n\n~~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~~\n\n* `Getting Started With TensorFlow`_: This guide gets you started programming in TensorFlow\n* `learning tensorflow`_:A great resource to start\n* `Welcome to TensorFlow World`_: A simple and concise start to TensorFLow\n\n.. _learning tensorflow: http://learningtensorflow.com/getting_started/\n.. _Getting Started With TensorFlow: https://www.tensorflow.org/get_started/get_started\n.. _Welcome to TensorFlow World: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/0-welcome\n\n~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~\n\n* `Gentlest Introduction to Tensorflow `_\n* `TensorFlow in 5 Minutes `_\n* `Deep Learning with TensorFlow - Introduction to TensorFlow `_\n* `TensorFlow Tutorial (Sherry Moore, Google Brain) `_\n* `Deep Learning with Neural Networks and TensorFlow Introduction `_\n* `A fast with TensorFlow `_\n\n--------------------------\nGoing Deeper in TensorFLow\n--------------------------\n\nAdvanced machine learning users can go deeper in TensorFlow in order to\n*hit the root*. Scratching the surface may never take us too further!\n\n\n~~~~~~~~~~~~~~~~~~\nWritten Resources\n~~~~~~~~~~~~~~~~~~\n\n* `TensorFlow Mechanics`_: More experienced machine learning users can dig more in TensorFlow\n* `Advanced TensorFlow`_: Advanced Tutorials in TensorFlow\n* `We Need to Go Deeper`_: A Practical Guide to Tensorflow and Inception\n* `Wide and Deep Learning - Better Together with TensorFlow`_: A tutorial by Google Research Blog\n\n\n\n\n.. _TensorFlow Mechanics: https://www.tensorflow.org/get_started/mnist/mechanics\n.. _Advanced TensorFlow: https://github.com/sjchoi86/advanced-tensorflow\n.. _We Need to Go Deeper: https://medium.com/initialized-capital/we-need-to-go-deeper-a-practical-guide-to-tensorflow-and-inception-50e66281804f\n.. _Wide and Deep Learning - Better Together with TensorFlow: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html\n\n\n~~~~~~~~~~~~~~~~\nVisual Resources\n~~~~~~~~~~~~~~~~\n\n* `TensorFlow DeepDive`_: More experienced machine learning users can dig more in TensorFlow\n* `Go Deeper - Transfer Learning`_: TensorFlow and Deep Learning\n* `Distributed TensorFlow - Design Patterns and Best Practices`_: A talk that was given at the Advanced Spark and TensorFlow Meetup\n* `Fundamentals of TensorFlow`_\n* `TensorFlow Wide and Deep - Advanced Classification the easy way`_\n* `Tensorflow and deep learning - without a PhD`_: A great tutorial on TensoFLow workflow\n\n\n\n.. _TensorFlow DeepDive: https://www.youtube.com/watch?v=T0H6zF3K1mc\n.. _Go Deeper - Transfer Learning: https://www.youtube.com/watch?v=iu3MOQ-Z3b4\n.. _Distributed TensorFlow - Design Patterns and Best Practices: https://www.youtube.com/watch?v=YAkdydqUE2c\n.. _Fundamentals of TensorFlow: https://www.youtube.com/watch?v=EM6SU8QVSlY\n.. _TensorFlow Wide and Deep - Advanced Classification the easy way: https://www.youtube.com/watch?v=WKgNNC0VLhM\n.. _Tensorflow and deep learning - without a PhD: https://www.youtube.com/watch?v=vq2nnJ4g6N0\n"
},
{
"path": "docs/source/credentials/CODE_OF_CONDUCT.rst",
"content": "Contributor Covenant Code of Conduct\n====================================\n\nOur Pledge\n----------\n\nIn the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.\n\nOur Standards\n-------------\n\nExamples of behavior that contributes to creating a positive environment include:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a professional setting\n\nOur Responsibilities\n--------------------\n\nProject maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.\n\nScope\n-----\n\nThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\nEnforcement\n-----------\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at amirsina.torfi@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.\n\nAttribution\n------------\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/\n"
},
{
"path": "docs/source/credentials/CONTRIBUTING.rst",
"content": "\n*************\nContributing\n*************\n\n*For typos, please do not create a pull request. Instead, declare them in issues or email the repository owner*. Please note we have a code of conduct, please follow it in all your interactions with the project.\n\n====================\nPull Request Process\n====================\n\nPlease consider the following criterions in order to help us in a better way:\n\n1. The pull request is mainly expected to be a link suggestion.\n2. Please make sure your suggested resources are not obsolete or broken.\n3. Ensure any install or build dependencies are removed before the end of the layer when doing a\n build and creating a pull request.\n4. Add comments with details of changes to the interface, this includes new environment\n variables, exposed ports, useful file locations and container parameters.\n5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you\n do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.\n\n============\nFinal Note\n============\n\nWe are looking forward to your kind feedback. Please help us to improve this open source project and make our work better.\nFor contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate\nyour kind feedback and elaborate code inspections.\n"
},
{
"path": "docs/source/credentials/LICENSE.rst",
"content": "LICENSE\n========\n\nMIT License\n\nCopyright (c) 2019 Amirsina Torfi\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this book and associated documentation files (the \"source files\"), to deal\nin the product without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the product, and to permit persons to whom the book is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the product.\n\nTHE PRODUCT IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "docs/source/index.rst",
"content": ".. TensoFlow-World-Resources documentation master file, created by\n sphinx-quickstart on Wed Jul 5 16:28:45 2017.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\nWelcome to TensoFlow-World-Resources's documentation!\n=====================================================\n\n.. toctree::\n :maxdepth: 2\n :caption: Foreword\n\n intro/intro\n\n.. toctree::\n :maxdepth: 2\n :caption: Content\n\n content/warmup\n content/howtocode\n content/tutorials\n content/projects\n content/books\n\n.. toctree::\n :maxdepth: 2\n :caption: Document Credentials\n\n credentials/LICENSE\n"
},
{
"path": "docs/source/intro/intro.rst",
"content": "Introduction\n============\n\nThe purpose of this project is to introduce a shortcut to developers and researcher\nfor finding useful resources about TensorFlow.\n\n-----------\nMotivation\n-----------\n\nThere are different motivations for this open source project.\n\n~~~~~~~~~~~~~~~~~~~~~\nWhy using TensorFlow?\n~~~~~~~~~~~~~~~~~~~~~\n\nA deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms\nand designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of\nTensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of\nthe pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs\nsuch as `Keras `_ and `Slim `_\nwhich gather lots of the design puzzle pieces. The interesting point about TensorFlow is that **its trace can be found anywhere these days**.\nLots of the researchers and developers are using it and *its community is growing with the speed of light*! So the possible issues can\nbe overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community.\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nWhat's the point of this open source project?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThere other similar repositories similar to this repository and are very\ncomprehensive and useful and to be honest they made me ponder if there is\na necessity for this repository! A great example is `awesome-tensorflow `_\nrepository which is a curated list of different TensorFlow resources.\n\n**The point of this repository is that the resources are being targeted**. The organization\nof the resources is such that the user can easily find the things he/she is looking for.\nWe divided the resources to a large number of categories that in the beginning one may\nhave a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources.\nEven if someone doesn't know what to look for, in the beginning, the general resources have\nbeen provided.\n\n\n------------------------------------\nHow to make the most of this effort\n------------------------------------\n\nThe written and visual resources have been split. Moreover, As one can search\nin the documentation, the number of categories might look to be too much. For\nfinding the most relevant resources, please at first look through the general resources.\n"
},
{
"path": "docs/source/saved/conf (another copy).py",
"content": "import os\nimport sys\n\nimport sphinx_rtd_theme\nsys.path.insert(0, os.path.abspath('_ext'))\n# from recommonmark.parser import CommonMarkParser\n\nsys.path.insert(0, os.path.abspath('..'))\nsys.path.append(os.path.dirname(__file__))\n# os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"readthedocs.settings.dev\")\n\n# from django.conf import settings\n\n# import django\n# django.setup()\n\n\nsys.path.append(os.path.abspath('_ext'))\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.intersphinx',\n]\ntemplates_path = ['_templates']\n\nedit_on_github_project = 'astorfi/TensorFlow-World-Resources'\nedit_on_github_branch = 'master'\n\nsource_suffix = ['.rst', '.md']\n# source_parsers = {\n# '.md': CommonMarkParser,\n# }\n\nmaster_doc = 'index'\nproject = u'TensoFlow-World-Resources'\ncopyright = u'2017, Amirsina Torfi'\nauthor = u'Amirsina Torfi'\nversion = '1.0'\nrelease = '1.0'\nexclude_patterns = ['_build']\ndefault_role = 'obj'\npygments_style = 'sphinx'\n# intersphinx_mapping = {\n# 'TensorFlow-World': ('https://github.com/astorfi/TensorFlow-World', None),\n# }\nhtmlhelp_basename = 'ReadTheDocsdoc'\nlatex_documents = [\n (master_doc, 'TensoFlow-World-Resources.tex', u'TensoFlow-World-Resources Documentation',\n u'Amirsina Torfi', 'manual'),\n]\nman_pages = [\n (master_doc, 'tensoflow-world-resources', u'TensoFlow-World-Resources Documentation',\n [author], 1)\n]\nexclude_patterns = [\n # 'api' # needed for ``make gettext`` to not die.\n]\n\nlanguage = 'en'\n\nlocale_dirs = [\n 'locale/',\n]\ngettext_compact = False\n\nhtml_theme = 'sphinx_rtd_theme'\nhtml_static_path = ['_static']\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\nhtml_logo = '_img/tflogo.gif'\nhtml_theme_options = {\n 'logo_only': True,\n 'display_version': False,\n}\n\ngithub_url='https://github.com/astorfi/TensorFlow-World-Resources'\n\nhtml_context = {\n\"display_github\": True, # Add 'Edit on Github' link instead of 'View page source'\n\"last_updated\": True,\n\"commit\": False,\n 'github_url': 'https://github.com/spatialaudio/nbsphinx/'\n}\n\n\n#def setup(app):\n# app.add_stylesheet('custom.css')\n"
},
{
"path": "docs/source/saved/conf (copy).py",
"content": "import os\nimport sys\n\nimport sphinx_rtd_theme\nsys.path.insert(0, os.path.abspath('_ext'))\n# from recommonmark.parser import CommonMarkParser\n\nsys.path.insert(0, os.path.abspath('..'))\nsys.path.append(os.path.dirname(__file__))\n# os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"readthedocs.settings.dev\")\n\n# from django.conf import settings\n\n# import django\n# django.setup()\n\n\nsys.path.append(os.path.abspath('_ext'))\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.intersphinx',\n 'edit_on_github',\n]\ntemplates_path = ['_templates']\n\nedit_on_github_project = 'astorfi/TensorFlow-World-Resources'\nedit_on_github_branch = 'master'\n\nsource_suffix = ['.rst', '.md']\n# source_parsers = {\n# '.md': CommonMarkParser,\n# }\n\nmaster_doc = 'index'\nproject = u'TensoFlow-World-Resources'\ncopyright = u'2017, Amirsina Torfi'\nauthor = u'Amirsina Torfi'\nversion = '1.0'\nrelease = '1.0'\nexclude_patterns = ['_build']\ndefault_role = 'obj'\npygments_style = 'sphinx'\n# intersphinx_mapping = {\n# 'TensorFlow-World': ('https://github.com/astorfi/TensorFlow-World', None),\n# }\nhtmlhelp_basename = 'ReadTheDocsdoc'\nlatex_documents = [\n (master_doc, 'TensoFlow-World-Resources.tex', u'TensoFlow-World-Resources Documentation',\n u'Amirsina Torfi', 'manual'),\n]\nman_pages = [\n (master_doc, 'tensoflow-world-resources', u'TensoFlow-World-Resources Documentation',\n [author], 1)\n]\nexclude_patterns = [\n # 'api' # needed for ``make gettext`` to not die.\n]\n\nlanguage = 'en'\n\nlocale_dirs = [\n 'locale/',\n]\ngettext_compact = False\n\nhtml_theme = 'sphinx_rtd_theme'\nhtml_static_path = ['_static']\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\nhtml_logo = '_img/tflogo.gif'\nhtml_theme_options = {\n 'logo_only': True,\n 'display_version': False,\n}\n\ngithub_url='https://github.com/astorfi/TensorFlow-World-Resources'\n\nhtml_context = {\n\"display_github\": True, # Add 'Edit on Github' link instead of 'View page source'\n\"last_updated\": True,\n\"commit\": False,\n}\n\n\n#def setup(app):\n# app.add_stylesheet('custom.css')\n"
},
{
"path": "docs/source/saved/conf-2.py",
"content": "# -*- coding: utf-8 -*-\n\nimport os\nimport sys\n#\n# TensoFlow-World-Resources documentation build configuration file, created by\n# sphinx-quickstart on Wed Jul 5 16:28:45 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\n# import os\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\n\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = []\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\n# project = u'TensoFlow-World-Resources'\ncopyright = u'2017, Amirsina Torfi'\nauthor = u'Amirsina Torfi'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = u'0.1.0'\n# The full version, including alpha/beta/rc tags.\nrelease = u'0.1.0'\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.doctest',\n 'sphinx.ext.todo',\n 'sphinx.ext.viewcode',\n]\n\nautodoc_member_order = 'bysource'\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\nsys.path.insert(0, os.path.abspath('../../'))\n\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = []\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\n# todo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#html_theme = 'alabaster'\nhtml_theme = 'default'\n\n# on_rtd is whether we are on readthedocs.org\non_rtd = os.environ.get('READTHEDOCS', None) == 'True'\n\nif not on_rtd: # only import and set the theme if we're building docs locally\n import sphinx_rtd_theme\n html_theme = 'sphinx_rtd_theme'\n html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# otherwise, readthedocs.org uses their theme by default, so no need to specify it\n\n# html_theme = 'alabaster'\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\n\n# html_theme_options = {\n# 'show_powered_by': False,\n# 'github_user': 'astorfi',\n# 'github_repo': 'TensorFlow-World-Resources',\n# 'github_banner': True,\n# 'show_related': False\n# }\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'TensoFlow-World-Resourcesdoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n # 'papersize': 'letterpaper',\n\n # The font size ('10pt', '11pt' or '12pt').\n #\n # 'pointsize': '10pt',\n\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, 'TensoFlow-World-Resources.tex', u'TensoFlow-World-Resources Documentation',\n u'Amirsina Torfi', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n (master_doc, 'tensoflow-world-resources', u'TensoFlow-World-Resources Documentation',\n [author], 1)\n]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (master_doc, 'TensoFlow-World-Resources', u'TensoFlow-World-Resources Documentation',\n author, 'TensoFlow-World-Resources', 'One line description of project.',\n 'Miscellaneous'),\n]\n\n\n\n"
},
{
"path": "docs/source/saved/conf.py",
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Use sphinx-quickstart to create your own conf.py file!\n# After that, you have to edit a few things. See below.\n\nimport sphinx_py3doc_enhanced_theme\n\n# Select nbsphinx and, if needed, add a math extension (mathjax or pngmath):\nextensions = [\n 'nbsphinx',\n 'sphinx.ext.mathjax',\n]\n\n# Exclude build directory and Jupyter backup files:\nexclude_patterns = ['_build', '**.ipynb_checkpoints']\n\n# Default language for syntax highlighting in reST and Markdown cells\nhighlight_language = 'none'\n\n# Don't add .txt suffix to source files (available for Sphinx >= 1.5):\nhtml_sourcelink_suffix = ''\n\n# Execute notebooks before conversion: 'always', 'never', 'auto' (default)\n#nbsphinx_execute = 'never'\n\n# Use this kernel instead of the one stored in the notebook metadata:\n#nbsphinx_kernel_name = 'python3'\n\n# List of arguments to be passed to the kernel that executes the notebooks:\n#nbsphinx_execute_arguments = ['--InlineBackend.figure_formats={\"png\", \"pdf\"}']\n\n# If True, the build process is continued even if an exception occurs:\n#nbsphinx_allow_errors = True\n\n# Controls when a cell will time out (defaults to 30; use -1 for no timeout):\n#nbsphinx_timeout = 60\n\n# Default Pygments lexer for syntax highlighting in code cells:\n#nbsphinx_codecell_lexer = 'ipython3'\n\n# Width of input/output prompts used in CSS:\n#nbsphinx_prompt_width = '8ex'\n\n# If window is narrower than this, input/output prompts are on separate lines:\n#nbsphinx_responsive_width = '700px'\n\n# -- The settings below this line are not specific to nbsphinx ------------\n\nmaster_doc = 'index'\n\nproject = 'TensorFlow-World-Resources'\nauthor = 'Amirsina Torfi'\ncopyright = '2017, ' + author\n\nlinkcheck_ignore = [r'http://localhost:\\d+/']\n\n# -- Get version information from Git -------------------------------------\n\ntry:\n from subprocess import check_output\n release = check_output(['git', 'describe', '--tags', '--always'])\n release = release.decode().strip()\nexcept Exception:\n release = ''\n\n# -- Options for HTML output ----------------------------------------------\n\nhtml_title = project + ' version ' + release\nhtml_theme = \"sphinx_py3doc_enhanced_theme\"\nhtml_theme_path = [sphinx_py3doc_enhanced_theme.get_html_theme_path()]\nhtml_logo = '_img/tflogo.gif'\nhtml_theme_options = {\n 'githuburl': 'https://github.com/astorfi/TensorFlow-World-Resources',\n}\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n 'papersize': 'a4paper',\n 'preamble': r\"\"\"\n\\usepackage[sc,osf]{mathpazo}\n\\linespread{1.05} % see http://www.tug.dk/FontCatalogue/urwpalladio/\n\\renewcommand{\\sfdefault}{pplj} % Palatino instead of sans serif\n\\IfFileExists{zlmtt.sty}{\n \\usepackage[light,scaled=1.05]{zlmtt} % light typewriter font from lmodern\n}{\n \\renewcommand{\\ttdefault}{lmtt} % typewriter font from lmodern\n}\n\"\"\",\n}\n\nlatex_documents = [\n (master_doc, 'nbsphinx.tex', project, author, 'howto'),\n]\n\nlatex_show_urls = 'footnote'\n\n\n\n\n###########################################################################\n# auto-created readthedocs.org specific configuration #\n###########################################################################\n\n\n#\n# The following code was added during an automated build on readthedocs.org\n# It is auto created and injected for every build. The result is based on the\n# conf.py.tmpl file found in the readthedocs.org codebase:\n# https://github.com/rtfd/readthedocs.org/blob/master/readthedocs/doc_builder/templates/doc_builder/conf.py.tmpl\n#\n\n\nimport sys\nimport os.path\nfrom six import string_types\n\nfrom sphinx import version_info\n\n# Get suffix for proper linking to GitHub\n# This is deprecated in Sphinx 1.3+,\n# as each page can have its own suffix\nif globals().get('source_suffix', False):\n if isinstance(source_suffix, string_types):\n SUFFIX = source_suffix\n else:\n SUFFIX = source_suffix[0]\nelse:\n SUFFIX = '.rst'\n\n# Add RTD Static Path. Add to the end because it overwrites previous files.\nif not 'html_static_path' in globals():\n html_static_path = []\nif os.path.exists('_static'):\n html_static_path.append('_static')\nhtml_static_path.append('/home/docs/checkouts/readthedocs.org/readthedocs/templates/sphinx/_static')\n\n# Add RTD Theme only if they aren't overriding it already\nusing_rtd_theme = False\nif 'html_theme' in globals():\n if html_theme in ['default']:\n # Allow people to bail with a hack of having an html_style\n if not 'html_style' in globals():\n import sphinx_rtd_theme\n html_theme = 'sphinx_rtd_theme'\n html_style = None\n html_theme_options = {}\n if 'html_theme_path' in globals():\n html_theme_path.append(sphinx_rtd_theme.get_html_theme_path())\n else:\n html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n using_rtd_theme = True\nelse:\n import sphinx_rtd_theme\n html_theme = 'sphinx_rtd_theme'\n html_style = None\n html_theme_options = {}\n if 'html_theme_path' in globals():\n html_theme_path.append(sphinx_rtd_theme.get_html_theme_path())\n else:\n html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n using_rtd_theme = True\n\nif globals().get('websupport2_base_url', False):\n websupport2_base_url = 'https://readthedocs.org/websupport'\n if 'http' not in settings.MEDIA_URL:\n websupport2_static_url = 'https://media.readthedocs.org/static/'\n else:\n websupport2_static_url = 'https://media.readthedocs.org//static'\n\n\n#Add project information to the template context.\ncontext = {\n 'using_theme': using_rtd_theme,\n 'html_theme': html_theme,\n 'current_version': \"py3doc-enh-theme\",\n 'MEDIA_URL': \"https://media.readthedocs.org/\",\n 'PRODUCTION_DOMAIN': \"readthedocs.org\",\n 'versions': [\n (\"latest\", \"/en/latest/\"),\n (\"traditional-theme\", \"/en/traditional-theme/\"),\n (\"rtd-theme\", \"/en/rtd-theme/\"),\n (\"pyramid-theme\", \"/en/pyramid-theme/\"),\n (\"py3doc-enh-theme\", \"/en/py3doc-enh-theme/\"),\n (\"nature-theme\", \"/en/nature-theme/\"),\n (\"jupyter-theme\", \"/en/jupyter-theme/\"),\n (\"julia-theme\", \"/en/julia-theme/\"),\n (\"haiku-theme\", \"/en/haiku-theme/\"),\n (\"guzzle-theme\", \"/en/guzzle-theme/\"),\n (\"dotted-theme\", \"/en/dotted-theme/\"),\n (\"conda-again\", \"/en/conda-again/\"),\n (\"cloud-theme\", \"/en/cloud-theme/\"),\n (\"classic-theme\", \"/en/classic-theme/\"),\n (\"bootstrap-theme\", \"/en/bootstrap-theme/\"),\n (\"bizstyle-theme\", \"/en/bizstyle-theme/\"),\n (\"better-theme\", \"/en/better-theme/\"),\n (\"basicstrap-theme\", \"/en/basicstrap-theme/\"),\n (\"alabaster-theme\", \"/en/alabaster-theme/\"),\n (\"agogo-theme\", \"/en/agogo-theme/\"),\n ],\n 'downloads': [ \n (\"pdf\", \"//readthedocs.org/projects/nbsphinx/downloads/pdf/py3doc-enh-theme/\"),\n (\"htmlzip\", \"//readthedocs.org/projects/nbsphinx/downloads/htmlzip/py3doc-enh-theme/\"),\n ],\n 'subprojects': [ \n ],\n 'slug': 'nbsphinx',\n 'name': u'nbsphinx',\n 'rtd_language': u'en',\n 'canonical_url': 'http://nbsphinx.readthedocs.io/en/0.2.14/',\n 'analytics_code': '',\n 'single_version': False,\n 'conf_py_path': '/doc/',\n 'api_host': 'https://readthedocs.org',\n 'github_user': 'astorfi',\n 'github_repo': 'TensorFlow-World-Resources',\n 'github_version': 'py3doc-enh-theme',\n 'display_github': True,\n 'bitbucket_user': 'None',\n 'bitbucket_repo': 'None',\n 'bitbucket_version': 'py3doc-enh-theme',\n 'display_bitbucket': False,\n 'READTHEDOCS': True,\n 'using_theme': (html_theme == \"default\"),\n 'new_theme': (html_theme == \"sphinx_rtd_theme\"),\n 'source_suffix': SUFFIX,\n 'user_analytics_code': '',\n 'global_analytics_code': 'UA-17997319-1',\n \n 'commit': 'e949221b',\n \n}\nif 'html_context' in globals():\n html_context.update(context)\nelse:\n html_context = context\n\n# Add custom RTD extension\nif 'extensions' in globals():\n extensions.append(\"readthedocs_ext.readthedocs\")\nelse:\n extensions = [\"readthedocs_ext.readthedocs\"]\n"
},
{
"path": "requirements.txt",
"content": "python-coveralls\nsphinx_rtd_theme\n"
}
]![](\"https://github.com/astorfi/TensorFlow-World-Resources/blob/master/_img/mainpage/utility.png\")
\n.. \n..