gyunggyung / pytorch

PyTorch tutorials A to Z

Contents

1. PyTorch Basic

1. basic
2. variable autograd
3. Linear Regression Models
4. NonLinear Models
5. Classification Models
6. Batch Tranining

2. Neural Network Basic

1. Optimizers
2. NN MNIST
3. Deep NN MNIST
4. CNN MNIST
5. Dropout
6. RNN

3. Vison

1. CNN CIFAR-10
2. Transfer Learning
3. CNN High resolution images
4. DCGAN
5. VAE

4. NLP

1. Word Embedding
2. Text classificationw
3. NNLMW
4. Seq2Seq
5. RNN text generation
6. Reinforcement learning text generation
7. ELMO
8. Transformer
9. BERT
10. GPT-2
11. Transformer-XL
12. XLNet
13. T5

System requirements

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt

Getting Started

$ git clone https://github.com/gyunggyung/PyTorch.git
$ cd PyTorch
$ jupyter notebook

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Other Github

tutorial

• tutorials
• pytorch-tutorial
• Awesome-pytorch-list
• pytorch_geometric
• PyTorch-Tutorial
• paperswithcode

book

• pytorch-handbook
• pytorch-book
• nlp_with_pytorch_examples
• PyTorch_firststep

Vison

• StyleGAN_PyTorch
• hashtag-prediction-pytorch
• stargan-v2
• glow -albumentations
• kill-the-bits
• gen-efficientnet-pytorch

NLP

• pytorch-seq2seq
• transformers
• fairseq
• KorQuAD
• R-BERT
• JointBERT
• Korean_NER_CNN_BiLSTM
• pytorch-bert-crf-ner
• bert-event-extraction
• InvariantRiskMinimization
• V2V-PoseNet-pytorch
• mrqa
• vizseq
• nlp-tutorial
• OpenNMT-py
• mrc-for-flat-nested-ner
• mt-dnn

Speech

• deepspeech
• nonparaSeq2seqVC_code

recommender models

• spotlight
• PPLM
• dlrm

Recommended Reading

PyTorch basics

• http://pytorch.org/ For installation instructions
• Offical PyTorch tutorials for more tutorials (some of these tutorials are included there)
• Deep Learning with PyTorch: A 60-minute Blitz to get started with PyTorch in general
• Introduction to PyTorch for former Torchies if you are a former Lua Torch user
• jcjohnson's PyTorch examples for a more in depth overview (including custom modules and autograd functions)

Recurrent Neural Networks

• The Unreasonable Effectiveness of Recurrent Neural Networks shows a bunch of real life examples
• Deep Learning, NLP, and Representations for an overview on word embeddings and RNNs for NLP
• Understanding LSTM Networks is about LSTMs work specifically, but also informative about RNNs in general

Machine translation

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Sequence to Sequence Learning with Neural Networks

Attention models

• Neural Machine Translation by Jointly Learning to Align and Translate
• Effective Approaches to Attention-based Neural Machine Translation

Other RNN uses

• A Neural Conversational Model

Other PyTorch tutorials

• Deep Learning For NLP In PyTorch

Author

gyung/ @gyunggyung

pytorch's Issues

There's a trivial typo in 03_Linear_Regression_Models

In "In [103]"(Test Multivariate Linear model), I think that x[1] should be x[i].

I mean,
In [103]:

accuracy_list = []
for i,real_y in enumerate(y):
    accuracy = (mv_model((x[1])).data.numpy() - real_y.data.numpy())
    accuracy_list.append(np.absolute(accuracy))

for accuracy in accuracy_list:
    print(accuracy)

print("sum accuracy : ",sum(accuracy_list))
print("avg accuracy : ",sum(accuracy_list)/len(y))

should be

accuracy_list = []
for i,real_y in enumerate(y):
    accuracy = (mv_model((x[i])).data.numpy() - real_y.data.numpy())
    accuracy_list.append(np.absolute(accuracy))

for accuracy in accuracy_list:
    print(accuracy)

print("sum accuracy : ",sum(accuracy_list))
print("avg accuracy : ",sum(accuracy_list)/len(y))

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check it please.

Can't open 10_CNN_MNIST.ipynb

Could you check the file?

I can