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PyTorch Tutorial for Deep Learning Researchers

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PyTorch Tutorial for Deep Learning Researchers

-------------------------------------------------------------------------------- This repository provides tutorial code for deep learning researchers to learn [PyTorch](https://github.com/pytorch/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](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html).
## Table of Contents #### 1. Basics * [PyTorch Basics](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/01-basics/pytorch_basics/main.py) * [Linear Regression](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/01-basics/linear_regression/main.py#L22-L23) * [Logistic Regression](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/01-basics/logistic_regression/main.py#L33-L34) * [Feedforward Neural Network](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/01-basics/feedforward_neural_network/main.py#L37-L49) #### 2. Intermediate * [Convolutional Neural Network](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate/convolutional_neural_network/main.py#L35-L56) * [Deep Residual Network](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate/deep_residual_network/main.py#L76-L113) * [Recurrent Neural Network](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate/recurrent_neural_network/main.py#L39-L58) * [Bidirectional Recurrent Neural Network](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py#L39-L58) * [Language Model (RNN-LM)](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate/language_model/main.py#L30-L50) #### 3. Advanced * [Generative Adversarial Networks](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/generative_adversarial_network/main.py#L41-L57) * [Variational Auto-Encoder](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/variational_autoencoder/main.py#L38-L65) * [Neural Style Transfer](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/03-advanced/neural_style_transfer) * [Image Captioning (CNN-RNN)](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/03-advanced/image_captioning) #### 4. Utilities * [TensorBoard in PyTorch](https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/04-utils/tensorboard)
## Getting Started ```bash $ git clone https://github.com/yunjey/pytorch-tutorial.git $ cd pytorch-tutorial/tutorials/PATH_TO_PROJECT $ python main.py ```
## Dependencies * [Python 2.7 or 3.5+](https://www.continuum.io/downloads) * [PyTorch 0.4.0+](http://pytorch.org/)

核心特点

  • •PyTorch Basics
  • •Linear Regression
  • •Logistic Regression
  • •Feedforward Neural Network
  • •Convolutional Neural Network
  • •Deep Residual Network
  • •Recurrent Neural Network
  • •Bidirectional Recurrent Neural Network
  • •Language Model (RNN-LM)
  • •Generative Adversarial Networks

> 标签

Pythondeep-learningneural-networkspytorchpytorch-tutorial

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> 工具信息

发布日期2026年8月1日
最后更新2026年9月9日
分类编程语言
定价开源

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