TensorFlow2.0_Eager_Execution_Tutorials

This repository provides tutorial code of TensorFlow2.0 . This tutorials refer to the PyTorch tutorials

https://github.com/yunjey/pytorch-tutorial

Table of Contents

0. Low Level

• Using_tf.Variable
• TF_Eager Basics
• eager_vs_graph

1. Basics

• KerasAPI Sequential_and_Model
• Linear Regression
• Logistic Regression
• Feedforward Neural Network

2. Intermediate

• Convolutional Neural Network
• Convolutional Neural Network using Keras API
• Deep Residual Network
• Deep Residual Network using Keras API
• Recurrent Neural Network using Keras API
• Bidirectional Recurrent Neural Network using Keras API
• Language Model (RNN-LM)

3. Advanced

• Generative Adversarial Network
• Variational Auto-Encoder
• Graph Convolution
• Neural Style Transfer
• Image Captioning (CNN-RNN)

4. Probability

• MCMC Regression with JointDistribution
• Variational Regression with JointDistribution
• basic modeling with tfp.layers

X. Others

• Hyper Parameter Optmization
• eager vs pytorch speed
• learning discontinuity
• dropout bayes neural network

Dependencies

My environment is Google Colab or

Python 3.7

TensorFlow 2.0 TensorFlow Probability 0.9.0 (nightly)