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This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

Table of Contents

PPTs

• PyTorchZeroToAll

Videos

• PyTorchZeroToAll-lecture1~lecture4-Chinese version

Install

• [PyTorch install]

Getting Installed for OSX pip 3.6 without coda

$ pip3 install http://download.pytorch.org/whl/torch-0.3.1-cp36-cp36m-macosx_10_7_x86_64.whl 
$ pip3 install torchvision 

Getting Installed for Linux pip 3.5 with coda9

$ pip3 install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp35-cp35m-linux_x86_64.whl
$ pip3 install torchvision 

1. Basics

• Lecture2:Linear Model
• Lecture3:Gradient_Descent
• Lecture4:Back_propagration
• Lecture5:Linear_regression
• Lecture6:Logistic_regression
• Lecture7:Wide_Deep
• Lecture8:DataLoader
• Lecture8:DataLoader_logistic
• Lecture9:Softmax_Classifier
• Lecture9:Softmax_Classifier_mnist

2. Intermediate

• Convolutional Neural Network
• Deep Residual Network
• Recurrent Neural Network
• Bidirectional Recurrent Neural Network
• Language Model (RNN-LM)
• Generative Adversarial Network

3. Advanced

• Image Captioning (CNN-RNN)
• Deep Convolutional GAN (DCGAN)
• Variational Auto-Encoder
• Neural Style Transfer

4. Utilities

• TensorBoard in PyTorch

Getting Started

$ git clone https://github.com/Tim810306/PytorchTutorial.git
$ cd PytorchTutorial/tutorials/project_path
$ python main.py               # cpu version
$ python main-gpu.py           # gpu version
$ python main_XXX.py           # execute XXX for cpu version

Dependencies

• Python 2.7 or 3.5
• PyTorch 0.1.12

Author

Cheng Yu Ting/ @Tim810306