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TensorFlow-World-Resources - Project Page_

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============ Introduction

The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about TensorFlow.


There are different motivations for this open source project.

Why using TensorFlow?

A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras <>_ and Slim <>_ which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community.

What's the point of this open source project?

There other similar repositories similar to this repository and are very comprehensive and useful and to be honest they made me ponder if there is a necessity for this repository! A great example is awesome-tensorflow <>_ repository which is a curated list of different TensorFlow resources.

The point of this repository is that the resources are being targeted. The organization of the resources is such that the user can easily find the things he/she is looking for. We divided the resources to a large number of categories that in the beginning one may have a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources. Even if someone doesn't know what to look for, in the beginning, the general resources have been provided.

How to make the most of this effort

The written and visual resources have been split. Moreover, As one can search in the documentation, the number of categories might look to be too much. For finding the most relevant resources, please at first look through the general resources.

============================ Entrance to TensorFlow World

In this section, different TensorFlow topics and their associated resources will be addressed.


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First of all, the TensorFlow must be installed!

  • Installing TensorFlow_: Official TensorFLow installation
  • Install TensorFlow from the source_: A comprehensive guide on how to install TensorFlow from the source using python/anaconda
  • TensorFlow Installation_: A short TensorFlow installation guide powered by NVIDIA
  • 7 SIMPLE STEPS TO INSTALL TENSORFLOW ON WINDOWS_: A concise tutorial for installing TensorFlow on Windows

.. _Installing TensorFlow: .. _Install TensorFlow from the source: .. _TensorFlow Installation: .. _7 SIMPLE STEPS TO INSTALL TENSORFLOW ON WINDOWS:

  • Install TensorFlow on Ubuntu_: A comprehensive tutorial on how to install TensorFlow on Ubuntu
  • Installation of TensorFlow_: The video covers how to setup TensorFlow
  • Installing CPU and GPU TensorFlow on Windows_: A tutorial on TensorFlow installation for Windows
  • Installing the GPU version of TensorFlow for making use of your CUDA GPU_: A GPU-targeted TensoFlow installation

.. _Install TensorFlow on Ubuntu: .. _Installation of TensorFlow: .. _Installing CPU and GPU TensorFlow on Windows: .. _Installing the GPU version of TensorFlow for making use of your CUDA GPU:

Getting Started

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This part points to resources on how to start to code with TensorFLow

  • Getting Started With TensorFlow Framework_: This guide gets you started programming in TensorFlow
  • learning TensorFlow Deep Learning_:A great resource to start
  • Welcome to TensorFlow World_: A simple and concise start to TensorFLow

.. _learning TensorFlow Deep Learning: .. _Getting Started With TensorFlow Framework: .. _Welcome to TensorFlow World:

  • Gentlest Introduction to Tensorflow <>_
  • TensorFlow in 5 Minutes <>_
  • Deep Learning with TensorFlow - Introduction to TensorFlow <>_
  • TensorFlow Tutorial (Sherry Moore, Google Brain) <>_
  • Deep Learning with Neural Networks and TensorFlow Introduction <>_
  • A fast with TensorFlow <https:/>_

Going Deeper in TensorFLow

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Advanced machine learning users can go deeper in TensorFlow in order to hit the root. Scratching the surface may never take us too further!

  • TensorFlow Mechanics_: More experienced machine learning users can dig more in TensorFlow
  • Advanced TensorFlow_: Advanced Tutorials in TensorFlow
  • We Need to Go Deeper_: A Practical Guide to Tensorflow and Inception
  • Wide and Deep Learning - Better Together with TensorFlow_: A tutorial by Google Research Blog

.. _TensorFlow Mechanics: .. _Advanced TensorFlow: .. _We Need to Go Deeper: .. _Wide and Deep Learning - Better Together with TensorFlow:

  • TensorFlow DeepDive_: More experienced machine learning users can dig more in TensorFlow
  • Go Deeper - Transfer Learning_: TensorFlow and Deep Learning
  • Distributed TensorFlow - Design Patterns and Best Practices_: A talk that was given at the Advanced Spark and TensorFlow Meetup
  • Distributed TensorFlow Guide_
  • Fundamentals of TensorFlow_
  • TensorFlow Wide and Deep - Advanced Classification the easy way_
  • Tensorflow and deep learning - without a PhD_: A great tutorial on TensoFLow workflow

.. _TensorFlow DeepDive: .. _Go Deeper - Transfer Learning: .. _Distributed TensorFlow - Design Patterns and Best Practices: .. _Distributed TensorFlow Guide: .. _Fundamentals of TensorFlow: .. _TensorFlow Wide and Deep - Advanced Classification the easy way: .. _Tensorflow and deep learning - without a PhD:

============================ Programming with TensorFlow

The references here, deal with the details of programming and writing TensorFlow code.

Reading data and input pipeline

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The first part is always how to prepare data and how to provide the pipeline to feed it to TensorFlow. Usually providing the input pipeline can be complicated, even more than the structure design!

  • Dataset API for TensorFlow Input Pipelines_: A TensorFlow official documentation on Using the Dataset API for TensorFlow Input Pipelines
  • TesnowFlow input pipeline_: Input pipeline provided by Stanford.
  • TensorFlow input pipeline example_: A working example.
  • TensorFlow Data Input_: TensorFlow Data Input: Placeholders, Protobufs & Queues
  • Reading data_: The official documentation by the TensorFLow on how to read data
  • basics of reading a CSV file_: A tutorial on reading a CSV file
  • Custom Data Readers_: Official documentation on this how to define a reader.

.. _Dataset API for TensorFlow Input Pipelines: .. _TesnowFlow input pipeline: .. _TensorFlow input pipeline example: .. _TensorFlow Data Input: .. _Reading data: .. _basics of reading a CSV file: .. _Custom Data Readers:

  • Tensorflow tutorial on TFRecords_: A tutorial on how to transform data into TFRecords

.. _Tensorflow tutorial on TFRecords:

  • An introduction to TensorFlow queuing and threading_: A tutorial on how to understand and create queues an efficient pipelines

.. _An introduction to TensorFlow queuing and threading:


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Variables are supposed to hold the parameters and supersede by new values as the parameters are updated. Variables must be clearly set and initialized.

Creation, Initialization
  • Variables Creation and Initialization_: An official documentation on setting up variables
  • Introduction to TensorFlow Variables - Creation and Initialization_: This tutorial deals with defining and initializing TensorFlow variables
  • Variables_: An introduction to variables

.. _Variables Creation and Initialization: .. _Introduction to TensorFlow Variables - Creation and Initialization: .. _Variables:

Saving and restoring
  • Saving and Loading Variables_: The official documentation on saving and restoring variables
  • save and restore Tensorflow models_: A quick tutorial to save and restore Tensorflow models

.. _Saving and Loading Variables: .. _save and restore Tensorflow models:

Sharing Variables
  • Sharing Variables_: The official documentation on how to share variables

.. _Sharing Variables:

  • Deep Learning with Tensorflow - Tensors and Variables_: A Tensorflow tutorial for introducing Tensors, Variables and Placeholders
  • Tensorflow Variables_: A quick introduction to TensorFlow variables
  • Save and Restore in TensorFlow_: TensorFlow Tutorial on Save and Restore variables

.. _Deep Learning with Tensorflow - Tensors and Variables: .. _Tensorflow Variables: .. _Save and Restore in TensorFlow:

TensorFlow Utilities

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Different utilities empower TensorFlow for faster computation in a more monitored manner.

  • Supervisor - Training Helper for Days-Long Trainings_: The official documentation for TensorFLow Supervisor.
  • Using TensorFlow Supervisor with TensorBoard summary groups_: Using both TensorBoard and the Supervisor for profit
  • Tensorflow example_: A TensorFlow example using Supervisor.

.. _Supervisor - Training Helper for Days-Long Trainings: .. _Using TensorFlow Supervisor with TensorBoard summary groups: .. _Tensorflow example:

TensorFlow Debugger
  • TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial_: Official documentation for using debugger for MNIST
  • How to Use TensorFlow Debugger with tf.contrib.learn_: A more high-level method to use the debugger.
  • Debugging TensorFlow Codes_: A Practical Guide for Debugging TensorFlow Codes
  • Debug TensorFlow Models with tfdbg_: A tutorial by Google Developers Blog

.. _TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial: .. _How to Use TensorFlow Debugger with tf.contrib.learn: .. _Debugging TensorFlow Codes: .. _Debug TensorFlow Models with tfdbg:

  • Exporting and Importing a MetaGraph_: Official TensorFlow documentation
  • Model checkpointing using meta-graphs in TensorFlow_: A working example

.. _Exporting and Importing a MetaGraph: .. _Model checkpointing using meta-graphs in TensorFlow:

  • TensorBoard - Visualizing Learning_: Official documentation by TensorFlow.
  • TensorFlow Ops_: Provided by Stanford
  • Visualisation with TensorBoard_: A tutorial on how to create and visualize a graph using TensorBoard
  • Tensorboard_: A brief tutorial on Tensorboard

.. _TensorBoard - Visualizing Learning: .. _TensorFlow Ops: .. _Visualisation with TensorBoard: .. _Tensorboard:

  • Hands-on TensorBoard (TensorFlow Dev Summit 2017)_: An introduction to the amazing things you can do with TensorBoard
  • Tensorboard Explained in 5 Min_: Providing the code for a simple handwritten character classifier in Python and visualizing it in Tensorboard
  • How to Use Tensorboard_: Going through a bunch of different features in Tensorboard

.. _Hands-on TensorBoard (TensorFlow Dev Summit 2017): .. _Tensorboard Explained in 5 Min: .. _How to Use Tensorboard:

==================== TensorFlow Tutorials

This section is dedicated to provide tutorial resources on the implementation of different models with TensorFlow.

Linear and Logistic Regression

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  • TensorFlow Linear Model Tutorial_: Using TF.Learn API in TensorFlow to solve a binary classification problem
  • Linear Regression in Tensorflow_: Predicting house prices in Boston area
  • Linear regression with Tensorflow_: Make use of tensorflow for numeric computation using data flow graphs
  • Logistic Regression in Tensorflow with SMOTE_: Implementation of Logistic Regression in TensorFlow
  • A TensorFlow Tutorial - Email Classification_: Using a simple logistic regression classifier
  • Linear Regression using TensorFlow_: Training a linear model by TensorFlow
  • Logistic Regression using TensorFlow_: Training a logistic regression by TensorFlow for binary classification

.. _TensorFlow Linear Model Tutorial: .. _Linear Regression in Tensorflow: .. _Linear regression with Tensorflow: .. _Logistic Regression in Tensorflow with SMOTE: .. _A TensorFlow Tutorial - Email Classification: .. _Linear Regression using TensorFlow: .. _Logistic Regression using TensorFlow:

  • Deep Learning with Tensorflow - Logistic Regression_: A tutorial on Logistic Regression
  • Deep Learning with Tensorflow - Linear Regression with TensorFlow_: A tutorial on Linear Regression

.. _Deep Learning with Tensorflow - Logistic Regression: .. _Deep Learning with Tensorflow - Linear Regression with TensorFlow:

Convolutional Neural Networks

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  • Convolutional Neural Networks_: Official TensorFlow documentation
  • Convolutional Neural Networks using TensorFlow_: Training a classifier using convolutional neural networks
  • Image classifier using convolutional neural network_: Building a convolutional neural network based image classifier
  • Convolutional Neural Network CNN with TensorFlow tutorial_: It covers how to write a basic convolutional neural network within TensorFlow with Python
  • Deep Learning CNNs in Tensorflow with GPUs_: Designing the architecture of a convolutional neural network (CNN)

.. _Convolutional Neural Networks: .. _Convolutional Neural Networks using TensorFlow: .. _Image classifier using convolutional neural network: .. _Convolutional Neural Network CNN with TensorFlow tutorial: .. _Deep Learning CNNs in Tensorflow with GPUs:

  • Deep Learning with Neural Networks_: Convolutional Neural Networks with TensorFlow
  • TensorFlow Tutorial_: Convolutional Neural Network
  • Understanding Convolution with TensorFlow_: A tutorial on Convolution operation with TensorFlow
  • CNN - Deep Learning with Tensorflow_: Convolutional Network with TensorFlow

.. _Deep Learning with Neural Networks: .. _TensorFlow Tutorial: .. _Understanding Convolution with TensorFlow: .. _CNN - Deep Learning with Tensorflow:

Recurrent Neural Networks

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  • Recurrent Neural Networks_: TensorFlow official documentation
  • How to build a Recurrent Neural Network in TensorFlow_: How to build a simple working Recurrent Neural Network in TensorFlow
  • Recurrent Neural Networks in Tensorflow_: Building a vanilla recurrent neural network (RNN) from the ground up in Tensorflow
  • RNNs in Tensorflow - a Practical Guide and Undocumented Features_: Going over some of the best practices for working with RNNs in Tensorflow
  • RNN / LSTM cell example in TensorFlow and Python_: Covering how to code a Recurrent Neural Network model with an LSTM in TensorFlow
  • Sequence prediction using recurrent neural networks(LSTM) with TensorFlow_: How to approximate a sequence of vectors using a recurrent neural networks
  • TensorFlow RNN Tutorial_: Recurrent Neural Networks for exploring time series and developing speech recognition capabilities

.. _Recurrent Neural Networks: .. _How to build a Recurrent Neural Network in TensorFlow: .. _Recurrent Neural Networks in Tensorflow: .. _RNNs in Tensorflow - a Practical Guide and Undocumented Features: .. _RNN / LSTM cell example in TensorFlow and Python: .. _Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: .. _TensorFlow RNN Tutorial:

  • Deep Learning with Neural Networks and TensorFlow_: Recurrent Neural Networks (RNN)
  • An Introduction to LSTMs in Tensorflow_: A brief tutorial
  • Deep Learning with Tensorflow - The Recurrent Neural Network Model_: A tutorial on the Recurrent Neural Network Models
  • Sequence Models and the RNN API_: TensorFlow Dev Summit 2017
  • RNN Example in Tensorflow_: A quick tutorial

.. _Deep Learning with Neural Networks and TensorFlow: .. _An Introduction to LSTMs in Tensorflow: .. _Deep Learning with Tensorflow - The Recurrent Neural Network Model: .. _Sequence Models and the RNN API: .. _RNN Example in Tensorflow:


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  • Deep Autoencoder with TensorFlow_: An open source project
  • Variational Autoencoder in TensorFlow_: A tutorial on Variational Autoencoder
  • Diving Into TensorFlow With Stacked Autoencoders_: A nice brief tutorials
  • Convolutional Autoencoders in Tensorflow_: Implementing a single layer CAE
  • Variational Autoencoder using Tensorflow_: Facial expression low dimensional embedding

.. _Deep Autoencoder with TensorFlow: .. _Variational Autoencoder in TensorFlow: .. _Diving Into TensorFlow With Stacked Autoencoders: .. _Convolutional Autoencoders in Tensorflow: .. _Variational Autoencoder using Tensorflow:

  • Deep Learning with Tensorflow - Autoencoder Structure_: Tutorial on Autoencoder models
  • Deep Learning with Tensorflow - RBMs and Autoencoders_: Tutorial on Restricted Boltzmann machines and AEs

.. _Deep Learning with Tensorflow - Autoencoder Structure: .. _Deep Learning with Tensorflow - RBMs and Autoencoders:

Generative models

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  • Generative Adversarial Nets in TensorFlow_: Implementing GAN using TensorFlow, with MNIST data
  • Generative Adversarial Networks_: A working example of Generative Adversarial Networks

.. _Generative Adversarial Nets in TensorFlow: .. _Generative Adversarial Networks:

  • TensorFlow Tutorial - Adversarial Examples_: A tutorial on a working example for generative models

.. _TensorFlow Tutorial - Adversarial Examples: link

Multiple GPUs

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  • Using GPUs_: Official TensorFlow documentation
  • Deep Learning with Multiple GPUs on Rescale_: TensorFlow Tutorial

.. _Using GPUs: .. _Deep Learning with Multiple GPUs on Rescale:

=================== TensorFlow Projects

This section is dedicated to provide resources that are mainly open source projects developed by TensorFlow. Those might be comprehensive tutorials on working example.

Comprehensive Tutorials

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  • TensorFlow-World_: Concise and ready-to-use TensorFlow tutorials with detailed documentation
  • TensorFlow-Tutorials_: Introduction to deep learning based on Google's TensorFlow framework
  • TensorFlow Tutorials_: Organized tutorials in TensorFlow
  • TensorFlow-Examples_: Providing working examples in TensorFlow
  • Tensorflow Tutorials using Jupyter Notebook_: TensorFlow tutorials written in Python plus Jupyter Notebook

.. _TensorFlow-World: .. _TensorFlow-Tutorials: .. _TensorFlow Tutorials: .. _TensorFlow-Examples: .. _Tensorflow Tutorials using Jupyter Notebook:


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  • TensorFlow Models_: Machine learning models implemented in TensorFlow
  • Tensorflow VGG16 and VGG19_: Implementation of VGG 16 and VGG 19 based on tensorflow-vgg16 and Caffe to Tensorflow
  • ResNet in TensorFlow: Implementation of Deep Residual Learning for Image Recognition <>
  • Inception in TensorFlow_: Train the Inception v3 architecture
  • A TensorFlow implementation of DeepMind WaveNet paper: TensorFlow implementation of the WaveNet generative neural network architecture <> for audio generation
  • 3D Convolutional Neural Networks for Speaker Verification: Implementation of 3D Convolutional Neural Networks for Speaker Verification application <> in TensorFlow.
  • Domain Transfer Network (DTN): The implementation of Unsupervised Cross-Domain Image Generation <> in TensorFlow
  • Neural Style_: The Neural Style algorithm implementation that synthesizes a pastiche
  • SqueezeNet in TensorFlow_: Tensorflow implementation of SqueezeNet

.. _TensorFlow Models: .. _Tensorflow VGG16 and VGG19: .. _ResNet in TensorFlow: .. _Inception in TensorFlow: .. _A TensorFlow implementation of DeepMind WaveNet paper: .. _3D Convolutional Neural Networks for Speaker Verification: .. _Domain Transfer Network (DTN): .. _Neural Style: .. _SqueezeNet in TensorFlow:

=================== Published Resources

This section is dedicated to provide published resources on TensorFlow, Such as websites, blogs, and books.

Online Courses and Documentations

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  • LearningTensorFlow_: Beginner-level tutorials for a TensorFlow
  • Deep Learning by Google_: A free online course developed by Google and Udacity
  • Tensorflow for Deep Learning Research_: A comprehensive course by Stanford
  • Creative Applications of Deep Learning with TensorFlow_: A free online course on TensorFlow from Kadenze
  • Deep Learning with TensorFlow Tutorial_: In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow

.. _LearningTensorFlow: .. _Deep Learning by Google: .. _Tensorflow for Deep Learning Research: .. _Creative Applications of Deep Learning with TensorFlow: .. _Deep Learning with TensorFlow Tutorial:


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  • TensorFlow Machine Learning Cookbook_: Quick guide to implementing TensorFlow in your day-to-day machine learning activities
  • Deep Learning with TensorFlow_: Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems
  • First contact with TensorFlow_: An online book on TensorFlow
  • Building Machine Learning Projects with TensorFlow_: Learn how to implement TensorFlow in production
  • Learning TensorFlow_: This book is an end-to-end guide to TensorFlow
  • Machine Learning with TensorFlow_: Tackle common commercial machine learning problems with Google’s TensorFlow library
  • Getting Started with TensorFlow_: An easy-to-understand book on TensorFlow
  • 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
  • Machine Learning with TensorFlow (MEAP)_: An introduction to the concepts of TensorFlow

.. _TensorFlow Machine Learning Cookbook: .. _Deep Learning with TensorFlow: .. _First contact with TensorFlow: .. _Building Machine Learning Projects with TensorFlow: .. _Learning TensorFlow: .. _Machine Learning with TensorFlow: .. _Getting Started with TensorFlow: .. _Hands-On Machine Learning with Scikit-Learn and TensorFlow: .. _Machine Learning with TensorFlow (MEAP):

============ Contributing

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.

Pull Request Process

Please consider the following criterions in order to help us in a better way:

  1. The pull request is mainly expected to be a link suggestion.
  2. Please make sure your suggested resources are not obsolete or broken.
  3. Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
  4. Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
  5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.

Final Note

We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and support.

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