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MorvanZhou / PyTorch-Tutorial

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

8,447 3,096 Language: Jupyter Notebook License: MIT Updated: 21d ago

README

<br>

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 notebook files
which is a great contribution to this tutorial.

For Chinese speakers: All methods mentioned below have their video and text tutorial in Chinese.
Visit 莫烦 Python for more.
You can watch my Youtube channel as well.

Regression

<a href="tutorial-contents/301_regression.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/1-1-2.gif">
</a>

Classification

<a href="tutorial-contents/302_classification.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/1-1-3.gif">
</a>

CNN

<a href="tutorial-contents/401_CNN.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/4-1-2.gif" >
</a>

RNN

<a href="tutorial-contents/403_RNN_regressor.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/4-3-1.gif" >
</a>

Autoencoder

<a href="tutorial-contents/403_RNN_regressor.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/4-4-1.gif" >
</a>

<a href="tutorial-contents/403_RNN_regressor.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/4-4-2.gif" >
</a>

GAN (Generative Adversarial Nets)

<a href="tutorial-contents/406_GAN.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/4-6-1.gif" >
</a>

Dropout

<a href="tutorial-contents/503_dropout.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/5-3-1.gif" >
</a>

Batch Normalization

<a href="tutorial-contents/504_batch_normalization.py">
<img class="course-image" src="https://mofanpy.com/static/results/torch/5-4-2.gif" >
</a>

Donation

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

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