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1 | 1 | # A-Quick-and-Simple-Pytorch-Tutorial |
2 | 2 | This repo will contain simple tutorials for getting familiar with Pytorch quickly for beginners. |
| 3 | +This actually acts as a personal note for myself as well, as I review my old recollection of different algorithms and concepts |
| 4 | +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 |
| 5 | +find useful. |
| 6 | +I'll tidy things up when I get the time, the following section will be updated as I finish different parts. |
| 7 | +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. |
| 8 | +Hope this comes handy to some of you dear fellow software engineers/deeplearning researchers. |
| 9 | +Have a wonderful day/night :) |
| 10 | + |
| 11 | +Subjcts : |
| 12 | +- [x] Introduction to Pytorch basics |
| 13 | +- [x] Introduction on Networks: |
| 14 | + - [x] training and testing (including augmentation) |
| 15 | + - [x] changing and finetuning architectures |
| 16 | + - [x] saving and loading models |
| 17 | +- Autoencoders |
| 18 | + - [x] Autoencoder(AE) |
| 19 | + - [x] Deep MLP Autoencoder(MLPAE) |
| 20 | + - [x] Convolutional Autoencoder(ConvAE) |
| 21 | + - [x] Sparse Autoencoder(SAE) (l1penalty, kldivergance) |
| 22 | + - [x] Denoising Autoencoder(DAE) |
| 23 | + - [x] Contractive Autoencoder(CAE) |
| 24 | + - [x] Variational Autoencoder(VAE) |
| 25 | + - [x] Conditional Variational Autoencoder(Cond-VAE) |
| 26 | + - [x] Disentagled(beta) Variational Autoencoder(B-VAE) |
| 27 | + - To do: |
| 28 | + - Sequence to Sequence Autoencoder |
| 29 | + - Cyclical Annealing Schedule |
| 30 | +- [x] MultiTask Learning |
| 31 | +- [ ] GANs (GAN, DCGAN, CGAN, CycleGAN, StarGAN, StyleGAN, WGAN, etc) |
| 32 | +- [ ] RNNs(RNN, LSTM, GRU) (NLP and Vision) |
| 33 | + - [ ] Text Generation |
| 34 | + - [ ] Sentiment Analysis |
| 35 | + - [ ] Seq2Seq |
| 36 | + - [ ] Attention Mechanism |
| 37 | + - [ ] Transformers |
| 38 | + - [ ] Image Captioning |
| 39 | + - [ ] CTC Loss |
| 40 | + - [ ] Word Embedding |
| 41 | + - [ ] NER(Named Entity Recognition) |
| 42 | + - [ ] Misc |
| 43 | +- [ ] Style transfer |
| 44 | +- [ ] Adversarial Attacks (Examples) |
| 45 | +- [ ] Object Detection |
| 46 | +- [ ] Semantic Segmentation |
| 47 | +- [ ] Siamese Networks |
| 48 | +- [ ] Autograd introduction |
| 49 | +- [ ] Datasets Introduction |
| 50 | +- Misc |
| 51 | + - [ ] Concepts |
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