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

Build your neural network easy and fast, 莫烦Python中文教学
https://github.com/morvanzhou/pytorch-tutorial

autoencoder batch batch-normalization classification cnn dqn dropout gan generative-adversarial-network machine-learning neural-network python pytorch pytorch-tutorial pytorch-tutorials regression reinforcement-learning rnn tutorial

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Build your neural network easy and fast, 莫烦Python中文教学

• Host: GitHub
• URL: https://github.com/morvanzhou/pytorch-tutorial
• Owner: MorvanZhou
• License: mit
• Created: 2017-05-05T15:12:04.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-03-23T05:01:42.000Z (almost 2 years ago)
• Last Synced: 2025-01-08T18:06:35.302Z (13 days ago)
• Topics: autoencoder, batch, batch-normalization, classification, cnn, dqn, dropout, gan, generative-adversarial-network, machine-learning, neural-network, python, pytorch, pytorch-tutorial, pytorch-tutorials, regression, reinforcement-learning, rnn, tutorial
• Language: Jupyter Notebook
• Homepage: https://mofanpy.com/tutorials/machine-learning/torch/
• Size: 14.7 MB
• Stars: 8,196
• Watchers: 214
• Forks: 3,112
• Open Issues: 30
• Metadata Files:
  • Readme: README.md

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### If you'd like to use **Tensorflow**, no worries, I made a new **Tensorflow Tutorial** just like PyTorch. Here is the link: [https://github.com/MorvanZhou/Tensorflow-Tutorial](https://github.com/MorvanZhou/Tensorflow-Tutorial)

# pyTorch Tutorials

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years.

Thanks for [liufuyang's](https://github.com/liufuyang) [**notebook files**](tutorial-contents-notebooks)

which is a great contribution to this tutorial.

* pyTorch basic

  * [torch and numpy](tutorial-contents/201_torch_numpy.py)

  * [Variable](tutorial-contents/202_variable.py)

  * [Activation](tutorial-contents/203_activation.py)

* Build your first network

  * [Regression](tutorial-contents/301_regression.py)

  * [Classification](tutorial-contents/302_classification.py)

  * [An easy way](tutorial-contents/303_build_nn_quickly.py)

  * [Save and reload](tutorial-contents/304_save_reload.py)

  * [Train on batch](tutorial-contents/305_batch_train.py)

  * [Optimizers](tutorial-contents/306_optimizer.py)

* Advanced neural network

  * [CNN](tutorial-contents/401_CNN.py)

  * [RNN-Classification](tutorial-contents/402_RNN_classifier.py)

  * [RNN-Regression](tutorial-contents/403_RNN_regressor.py)

  * [AutoEncoder](tutorial-contents/404_autoencoder.py)

  * [DQN Reinforcement Learning](tutorial-contents/405_DQN_Reinforcement_learning.py)

  * [A3C Reinforcement Learning](https://github.com/MorvanZhou/pytorch-A3C)

  * [GAN (Generative Adversarial Nets)](tutorial-contents/406_GAN.py) / [Conditional GAN](tutorial-contents/406_conditional_GAN.py)

* Others (WIP)

  * [Why torch dynamic](tutorial-contents/501_why_torch_dynamic_graph.py)

  * [Train on GPU](tutorial-contents/502_GPU.py)

  * [Dropout](tutorial-contents/503_dropout.py)

  * [Batch Normalization](tutorial-contents/504_batch_normalization.py)

**For Chinese speakers: All methods mentioned below have their video and text tutorial in Chinese.

Visit [莫烦 Python](https://mofanpy.com/tutorials/) for more.

You can watch my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) as well.**

### [Regression](tutorial-contents/301_regression.py)



    



### [Classification](tutorial-contents/302_classification.py)



    



### [CNN](tutorial-contents/401_CNN.py)



    



### [RNN](tutorial-contents/403_RNN_regressor.py)



    



### [Autoencoder](tutorial-contents/404_autoencoder.py)



    





    



### [GAN (Generative Adversarial Nets)](tutorial-contents/406_GAN.py)



    



### [Dropout](tutorial-contents/503_dropout.py)



    



### [Batch Normalization](tutorial-contents/504_batch_normalization.py)



    



# Donation

*If this does help you, please consider donating to support me for better tutorials. Any contribution is greatly appreciated!*