Getting Started • Table of Contents • About • Acknowledgment • FAQ • Citing

A collection of various deep learning architectures, models, and tips for PyTorch.

This repository contains many deep learning algorithms and their applications, this is how I love deep learning.

• our code requires python >= 3.7, torch >= 1.0.

• numpy == 1.18.2, scipy == 1.4.1, scikit-learn == 0.22.2, matplotlib == 3.2.1.

pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html

• Multilayer Perceptrons

  • MLP in nn.Module

• Convolutional Neural Networks (CNN)

  • AlexNet
  • VGG

• Recurrent Neural Networks (RNN)

• Transformer

  • Bert
  • GPT-2

• Vision Transformer

  • ViT

• Diffusion Model

  • DDPM

• AutoEncoder (AE)

  • VAE

• Generative Adversarial Networks (GAN)

  • basic GAN
  • CycleGAN

• Graph Neural Networks (GNN)

  • DeepWalk
  • Node2Vec
  • Graph Convolutional network (GCN)
  • GraphSage

• Deep Reinforcement Learning

  • Deep Q Learning (DQN)
  • Reinforcement Learning with Model-Agnostic Meta-Learning

• Meta Learning

  • MAML

• Tips and Tricks

  • How to train a GAN?

Figure: Multilayer Perceptrons model of the learning process

One of the greatest assets of Deep Learning is the community and their contributions. A few of my favourite resources that pair well with the models and componenets here are listed below.

• Delip Rao., & Brain McMahan., (2019). Natural Language Processing with PyTorch. Sebastopol: O'Reilly Media,Inc.

• Tariq Rashid., (2018). Make Your Own Neural Network. Beijing: Posts & Telecom Press.

• Tariq Rashid., (2020). Make Your First GAN with Pytorch. Beijing: Posts & Telecom Press.

• GANs:
  • 6 GAN Architectures You Really Should Know

• PyTorch wrapper:

  • Pytorch Lightning

• Tensors operations:

  • einops

• GANs:

  • PyTorch-GAN
  • How to train a GAN?
  • PyTorch GANs Library

• Metric Learning:

  • PyTorch Metric Learning

• Image Augmentation:

  • albumentations (Fast Image Augmentation library.)

• Vision Transformer:

  • Vision Transformer - Pytorch

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@misc{xing_2019_dlic,
      author = {Ximing Xing},
      title = {Deep Learning in Action},
      year = {2019},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/ximingxing/Deep-Learning-in-Action}}
}