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A collection of deep learning models implemented in PyTorch

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Deep learning models in PyTorch

NOTE: This project is not actively maintained anymore. Feel free to send a PR or fork it.

This repository contains deep learning models built in PyTorch. Intended for learning PyTorch, this repo is made understandable for someone with basic python and deep learning knowledge. All kinds of important deep learning models are implemented here. Links to the papers are also given.

The structure for each project is consistent:

  • model.py - Contains the model of the neural network.
  • train.py - Preprocessing the data and training the network.
  • test.py - Infering from the trained network.
  • any other supporting files.

Common folders:

  • Trained folder - Trained models can will be saved here.
  • Datasets folder - Datasets downloaded will be stored here.

Best practices like PEP8, dropout, batch normalization, suitable optimizers are used.

IMP: For training on the CPU remove '.cuda()' wherever you find it.

Models

1. CNNs

2. RNNs

3. GANs

4. Reinforcement

5. Others

References:

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  • Jupyter Notebook 97.2%
  • Python 2.8%