TensorFlow Examples
This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2.
It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as
layers
, estimator
, dataset
, ...).
Update (05/16/2020): Moving all default examples to TF2. For TF v1 examples: check here.
Tutorial index
0  Prerequisite
1  Introduction

Hello World (notebook). Very simple example to learn how to print "hello world" using TensorFlow 2.0+.

Basic Operations (notebook). A simple example that cover TensorFlow 2.0+ basic operations.
2  Basic Models

Linear Regression (notebook). Implement a Linear Regression with TensorFlow 2.0+.

Logistic Regression (notebook). Implement a Logistic Regression with TensorFlow 2.0+.

Word2Vec (Word Embedding) (notebook). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0+.

GBDT (Gradient Boosted Decision Trees) (notebooks). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset.
3  Neural Networks
Supervised

Simple Neural Network (notebook). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset.

Simple Neural Network (lowlevel) (notebook). Raw implementation of a simple neural network to classify MNIST digits dataset.

Convolutional Neural Network (notebook). Use TensorFlow 2.0+ 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset.

Convolutional Neural Network (lowlevel) (notebook). Raw implementation of a convolutional neural network to classify MNIST digits dataset.

Recurrent Neural Network (LSTM) (notebook). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API.

Bidirectional Recurrent Neural Network (LSTM) (notebook). Build a bidirectional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0+ 'layers' and 'model' API.

Dynamic Recurrent Neural Network (LSTM) (notebook). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0+ 'layers' and 'model' API.
Unsupervised

AutoEncoder (notebook). Build an autoencoder to encode an image to a lower dimension and reconstruct it.

DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.
4  Utilities

Save and Restore a model (notebook). Save and Restore a model with TensorFlow 2.0+.

Build Custom Layers & Modules (notebook). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0+ Models.

Tensorboard (notebook). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard.
5  Data Management

Load and Parse data (notebook). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...).

Build and Load TFRecords (notebook). Convert data into TFRecords format, and load them with TensorFlow 2.0+.

Image Transformation (i.e. Image Augmentation) (notebook). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training.
6  Hardware

MultiGPU Training (notebook). Train a convolutional neural network with multiple GPUs on CIFAR10 dataset.
TensorFlow v1
The tutorial index for TF v1 is available here: TensorFlow v1.15 Examples. Or see below for a list of the examples.
Dataset
Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples.
MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.
Official Website: http://yann.lecun.com/exdb/mnist/.
Installation
To download all the examples, simply clone this repository:
git clone https://github.com/aymericdamien/TensorFlowExamples
To run them, you also need the latest version of TensorFlow. To install it:
pip install tensorflow
or (with GPU support):
pip install tensorflow_gpu
For more details about TensorFlow installation, you can check TensorFlow Installation Guide
TensorFlow v1 Examples  Index
The tutorial index for TF v1 is available here: TensorFlow v1.15 Examples.
0  Prerequisite
1  Introduction

Hello World (notebook) (code). Very simple example to learn how to print "hello world" using TensorFlow.

Basic Operations (notebook) (code). A simple example that cover TensorFlow basic operations.

TensorFlow Eager API basics (notebook) (code). Get started with TensorFlow's Eager API.
2  Basic Models

Linear Regression (notebook) (code). Implement a Linear Regression with TensorFlow.

Linear Regression (eager api) (notebook) (code). Implement a Linear Regression using TensorFlow's Eager API.

Logistic Regression (notebook) (code). Implement a Logistic Regression with TensorFlow.

Logistic Regression (eager api) (notebook) (code). Implement a Logistic Regression using TensorFlow's Eager API.

Nearest Neighbor (notebook) (code). Implement Nearest Neighbor algorithm with TensorFlow.

KMeans (notebook) (code). Build a KMeans classifier with TensorFlow.

Random Forest (notebook) (code). Build a Random Forest classifier with TensorFlow.

Gradient Boosted Decision Tree (GBDT) (notebook) (code). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow.

Word2Vec (Word Embedding) (notebook) (code). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow.
3  Neural Networks
Supervised

Simple Neural Network (notebook) (code). Build a simple neural network (a.k.a Multilayer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation.

Simple Neural Network (tf.layers/estimator api) (notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multilayer Perceptron) to classify MNIST digits dataset.

Simple Neural Network (eager api) (notebook) (code). Use TensorFlow Eager API to build a simple neural network (a.k.a Multilayer Perceptron) to classify MNIST digits dataset.

Convolutional Neural Network (notebook) (code). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.

Convolutional Neural Network (tf.layers/estimator api) (notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.

Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.

Bidirectional Recurrent Neural Network (LSTM) (notebook) (code). Build a bidirectional recurrent neural network (LSTM) to classify MNIST digits dataset.

Dynamic Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.
Unsupervised

AutoEncoder (notebook) (code). Build an autoencoder to encode an image to a lower dimension and reconstruct it.

Variational AutoEncoder (notebook) (code). Build a variational autoencoder (VAE), to encode and generate images from noise.

GAN (Generative Adversarial Networks) (notebook) (code). Build a Generative Adversarial Network (GAN) to generate images from noise.

DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook) (code). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.
4  Utilities

Save and Restore a model (notebook) (code). Save and Restore a model with TensorFlow.

Tensorboard  Graph and loss visualization (notebook) (code). Use Tensorboard to visualize the computation Graph and plot the loss.

Tensorboard  Advanced visualization (notebook) (code). Going deeper into Tensorboard; visualize the variables, gradients, and more...
5  Data Management

Build an image dataset (notebook) (code). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file.

TensorFlow Dataset API (notebook) (code). Introducing TensorFlow Dataset API for optimizing the input data pipeline.

Load and Parse data (notebook). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...).

Build and Load TFRecords (notebook). Convert data into TFRecords format, and load them.

Image Transformation (i.e. Image Augmentation) (notebook). Apply various image augmentation techniques, to generate distorted images for training.
6  Multi GPU

Basic Operations on multiGPU (notebook) (code). A simple example to introduce multiGPU in TensorFlow.

Train a Neural Network on multiGPU (notebook) (code). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs.
More Examples
The following examples are coming from TFLearn, a library that provides a simplified interface for TensorFlow. You can have a look, there are many examples and prebuilt operations and layers.
Tutorials

TFLearn Quickstart. Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.
Examples