ybai62868/gluon-tutorial

Gluon Tutorial for Deep Learning Researchers && Engineers.

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This repository provides tutorial code for deep learning researchers && engineers to learn Gluon.
In the tutorial, most of the models were implemented with less than 50 lines of code. You will find Gluon and PyTorch share lots of similarities. After reading PyTorch to MXNet, you can easily convert your project between Pytorch and Gluon and learn both of them well. Before starting this tutorial, it is recommended to finish Crash Course. Then you can read Dive into Deep Learning to get a more comprehensive study.

Table of Contents

1. Basics

• • Gluon Basics
• • Linear Regression
• • Logistic Regression
• • Feedforward Neural Network

2. Intermediate

• • Convolutional Neural Network
• • Deep Residual Network
• • Densely Connected Network
• • Squeeze Network
• • Inception Network
• • Mobile Network
• • Very Deep Convolutional Network
• • Recurrent Neural Network
• • Bidirectional Recurrent Neural Network
• • Language Model (RNN-LM)

3. Advanced

• • Single Shot Detection
• • Fully Convolutional Network
• • Generative Adversarial Network
• • Variational Auto-Encoder
• • Neural Style Transfer
• • Facial Expression Recognition
• • Image Captioning (CNN-RNN)

4. Utilities

• • MXBoard in MXNet

Getting Started

$ git clone https://github.com/ybai62868/gluon-tutorial.git
$ cd gluon-tutorial/tutorials/PATH_TO_PROJECT
$ python main.py

Dependencies

• Python 3.6+
• MXNet 1.4.0

Author

Yang Bai / @YangBai

Note

Inspired by the pytorch-tutorial writen by @yunjey

Code will be continued.