This repo will contain simple tutorials for getting familiar with Pytorch quickly for beginners. This actually acts as a personal note for myself as well, as I review my old recollection of different algorithms and concepts in Pytorch. I tried to explain everything so in case later on I forget something, I can quickly recall it or see the refs I find useful. I'll tidy things up when I get the time, the following section will be updated as I finish different parts. Some sections are already done (e.g. syle transfer, RNNs, GANs, but they need full explanations, so when that is done, I'll push the changes to this repo. Hope this comes handy to some of you dear fellow software engineers/deeplearning researchers. Have a wonderful day/night :)
Subjcts :
- Introduction to Pytorch basics
- Introduction on Networks:
- training and testing (including augmentation)
- changing and finetuning architectures
- saving and loading models
- Autoencoders
- Autoencoder(AE)
- Deep MLP Autoencoder(MLPAE)
- Convolutional Autoencoder(ConvAE)
- Sparse Autoencoder(SAE) (l1penalty, kldivergance)
- Denoising Autoencoder(DAE)
- Contractive Autoencoder(CAE)
- Variational Autoencoder(VAE)
- Conditional Variational Autoencoder(Cond-VAE)
- Disentagled(beta) Variational Autoencoder(B-VAE)
- To do:
- Sequence to Sequence Autoencoder
- Cyclical Annealing Schedule
- MultiTask Learning
- GANs (GAN, DCGAN, CGAN, CycleGAN, StarGAN, StyleGAN, WGAN, etc)
- RNNs(RNN, LSTM, GRU) (NLP and Vision)
- Text Generation
- Sentiment Analysis
- Seq2Seq
- Attention Mechanism
- Transformers
- Image Captioning
- CTC Loss
- Word Embedding
- NER(Named Entity Recognition)
- Misc
- Style transfer
- Adversarial Attacks (Examples)
- Object Detection
- Semantic Segmentation
- Siamese Networks
- Autograd introduction
- Datasets Introduction
- Misc
- Concepts