README

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
- Introduction to Machine Learning.
- Introduction to MNIST Dataset.

 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 (low-level) (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 (low-level) (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.
- Bi-directional Recurrent Neural Network (LSTM) (notebook). Build a bi-directional 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
- Auto-Encoder (notebook). Build an auto-encoder to encode an image to a lower dimension and re-construct 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 bui

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