https://github.com/kyubyong/pytorch_exercises

https://github.com/kyubyong/pytorch_exercises

exercises neural-networks pytorch

Last synced: 1 day ago
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• Host: GitHub
• URL: https://github.com/kyubyong/pytorch_exercises
• Owner: Kyubyong
• License: mit
• Created: 2017-09-25T05:26:25.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-09-28T12:25:23.000Z (over 7 years ago)
• Last Synced: 2025-01-09T08:08:46.043Z (8 days ago)
• Topics: exercises, neural-networks, pytorch
• Language: Jupyter Notebook
• Size: 76.2 KB
• Stars: 316
• Watchers: 28
• Forks: 41
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Pytorch Exercises

Pytorch is one of the most popular deep learning libraries as of 2017. One possible way of familiarizing yourself with it, I think, is to practice with simple quizzes. That's where this project comes in. The outline will be as follows, though it's not fixed. If you find this helpful, you may like my other repos:


  * [NumPy exercises](https://github.com/Kyubyong/numpy_exercises)

  * [TensorFlow exercises](https://github.com/Kyubyong/tensorflow-exercises)

CHAPTER 1. Tensors

  * Tensor Creation ([Quiz](Chapter1_Tensors/Tensor_Creation.ipynb) / [Sol.](Chapter1_Tensors/Tensor_Creation_solution.ipynb))

  * Tensor Transformations ([Quiz](Chapter1_Tensors/Tensor_Transformations.ipynb) / [Sol.](Chapter1_Tensors/Tensor_Transformations_solution.ipynb))

  * Random Sampling ([Quiz](Chapter1_Tensors/Random_Sampling.ipynb) / [Sol.](Chapter1_Tensors/Random_Sampling_solution.ipynb))

  * Math Operations ([Quiz](Chapter1_Tensors/Math_Operations.ipynb) / [Sol.](Chapter1_Tensors/Math_Operations_solution.ipynb))

  * Statistics ([Quiz](Chapter1_Tensors/Statistics.ipynb) / [Sol.](Chapter1_Tensors/Statistics_solution.ipynb))

  * Linear Algebra ([Quiz](Chapter1_Tensors/Linear_Algebra.ipynb) / [Sol.](Chapter1_Tensors/Linear_Algebra_solution.ipynb))

  * Variables ([Quiz](Chapter1_Tensors/Variables.ipynb) / [Sol.](Chapter1_Tensors/Variables_solution.ipynb))

CHAPTER 2. Neural Networks

  * Linear Layers/Functions (WIP)

  * Convolution Layers/Functions (WIP)

  * Recurrent Layers (WIP)

  * Pooling Layers/Functions (WIP)

  * Dropout Layers/Functions (WIP)

  * Padding Layers (WIP)

  * Normalization Layers/Functions (WIP)

  * Activations (WIP)

  * Sparse Layers (WIP)

  * Loss (WIP)

  * Distance (WIP)

  * Initialization (WIP)

  * Optimization (WIP)

  * Utilities (WIP)

Sep., 2017


Created by Kyubyong


Reviewed by [yj](https://github.com/yjchoe)