https://github.com/astorfi/TensorFlowRoadmap
TensorFlowWorldResources – Project Page
Table of Contents
 Why using TensorFlow?
 What’s the point of this open source project?
 How to make the most of this effort
 Entrance to TensorFlow World
 Programming with TensorFlow
 Linear and Logistic Regression
 Convolutional Neural Networks
 Recurrent Neural Networks
 Comprehensive Tutorials
 Online Courses and Documentations
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 highlevel APIs such as
TensorFlowWorldResources – Project Page
Table of Contents
 Why using TensorFlow?
 What’s the point of this open source project?
 How to make the most of this effort
 Entrance to TensorFlow World
 Programming with TensorFlow
 Linear and Logistic Regression
 Convolutional Neural Networks
 Recurrent Neural Networks
 Comprehensive Tutorials
 Online Courses and Documentations
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 highlevel 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 awesometensorflow 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.
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
 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 GPUtargeted TensoFlow installation
This part points to resources on how to start to code with TensorFLow

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
 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 GPUtargeted TensoFlow installation
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
 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
Going Deeper in TensorFLow
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 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
Programming with TensorFlow
The references here, deal with the details of programming and writing TensorFlow code.
Reading data and input pipeline
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!

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 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
Programming with TensorFlow
The references here, deal with the details of programming and writing TensorFlow code.
Reading data and input pipeline
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.
 Tensorflow tutorial on TFRecords : A tutorial on how to transform data into TFRecords
 An introduction to TensorFlow queuing and threading : A tutorial on how to understand and create queues an efficient pipelines
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
 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
 Sharing Variables : The official documentation on how to share 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
Different utilities empower TensorFlow for faster computation in a more monitored manner.

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
 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
 Sharing Variables : The official documentation on how to share 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
Different utilities empower TensorFlow for faster computation in a more monitored manner.
 Supervisor – Training Helper for DaysLong 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.
 TensorFlow Debugger (tfdbg) CommandLineInterface Tutorial : Official documentation for using debugger for MNIST
 How to Use TensorFlow Debugger with tf.contrib.learn : A more highlevel 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
 Exporting and Importing a MetaGraph : Official TensorFlow documentation
 Model checkpointing using metagraphs in TensorFlow : A working example
 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
 Handson 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
This section is dedicated to provide tutorial resources on the implementation of different models with TensorFlow.
Linear and Logistic Regression
 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
 Deep Learning with Tensorflow – Logistic Regression : A tutorial on Logistic Regression
 Deep Learning with Tensorflow – Linear Regression with TensorFlow : A tutorial on Linear Regression
Convolutional Neural Networks

 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
 Deep Learning with Tensorflow – Logistic Regression : A tutorial on Logistic Regression
 Deep Learning with Tensorflow – Linear Regression with TensorFlow : A tutorial on Linear Regression
Convolutional Neural Networks
 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)
 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
Recurrent Neural Networks
 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
 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

 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
 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 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 Learning with Tensorflow – Autoencoder Structure : Tutorial on Autoencoder models
 Deep Learning with Tensorflow – RBMs and Autoencoders : Tutorial on Restricted Boltzmann machines and AEs
 Generative Adversarial Nets in TensorFlow : Implementing GAN using TensorFlow, with MNIST data
 Generative Adversarial Networks : A working example of Generative Adversarial Networks
 TensorFlow Tutorial – Adversarial Examples : A tutorial on a working example for generative models

 Generative Adversarial Nets in TensorFlow : Implementing GAN using TensorFlow, with MNIST data
 Generative Adversarial Networks : A working example of Generative Adversarial Networks
 TensorFlow Tutorial – Adversarial Examples : A tutorial on a working example for generative models
 Using GPUs : Official TensorFlow documentation
 Deep Learning with Multiple GPUs on Rescale : TensorFlow Tutorial
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
 TensorFlowWorld : Concise and readytouse TensorFlow tutorials with detailed documentation
 TensorFlowTutorials : Introduction to deep learning based on Google’s TensorFlow framework
 TensorFlow Tutorials : Organized tutorials in TensorFlow
 TensorFlowExamples : Providing working examples in TensorFlow
 Tensorflow Tutorials using Jupyter Notebook : TensorFlow tutorials written in Python plus Jupyter Notebook

 TensorFlowWorld : Concise and readytouse TensorFlow tutorials with detailed documentation
 TensorFlowTutorials : Introduction to deep learning based on Google’s TensorFlow framework
 TensorFlow Tutorials : Organized tutorials in TensorFlow
 TensorFlowExamples : Providing working examples in TensorFlow
 Tensorflow Tutorials using Jupyter Notebook : TensorFlow tutorials written in Python plus Jupyter Notebook
 TensorFlow Models : Machine learning models implemented in TensorFlow
 Tensorflow VGG16 and VGG19 : Implementation of VGG 16 and VGG 19 based on tensorflowvgg16 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 CrossDomain Image Generation in TensorFlow
 Neural Style : The Neural Style algorithm implementation that synthesizes a pastiche
 SqueezeNet in TensorFlow : Tensorflow implementation of SqueezeNet
This section is dedicated to provide published resources on TensorFlow, Such as websites, blogs, and books.
Online Courses and Documentations
 LearningTensorFlow : Beginnerlevel 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 : Beginnerlevel 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
 TensorFlow Machine Learning Cookbook : Quick guide to implementing TensorFlow in your daytoday 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 endtoend guide to TensorFlow
 Machine Learning with TensorFlow : Tackle common commercial machine learning problems with Google』s TensorFlow library
 Getting Started with TensorFlow : An easytounderstand book on TensorFlow
 HandsOn Machine Learning with ScikitLearn 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
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.
Please consider the following criterions in order to help us in a better way:
 The pull request is mainly expected to be a link suggestion.
 Please make sure your suggested resources are not obsolete or broken.
 Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
 Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
 You may merge the Pull Request in once you have the signoff 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.
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.