By studying a book named '파이토치 첫걸음', I organized what I learned about PyTorch in this repository.
Ch1,Ch2 don't include codes. So there are no files about this chapters.
The rest of the chapters are implemented in jupyter notebook named chapter name.
Plus, a file named practice is a file practiced based on what I learned from the this book.
- Concept of DeepLearning.
- install PyTorch.
Ch3.Linear Regression Analysis -> code link
- Basic tensor manipulation. (eg. torch.Tensor)
- implement Linear Regrssion using gradient descent. (by using torch.nn, torch.optim.. etc)
Ch4.ANN -> code link
- implement MLP, then solve non-Linear data. (by using nn.Sequential, nn.Linear, nn.ReLU.. etc)
- draw Learning curve through loss data.
Ch5.CNN -> code link
- implement CNN ,then classify MNIST images.
- implement famous vision models. (VGGNet, GoogLeNet, ResNet)
Ch6.RNN -> code link
- implement Simple RNN, LSTM, then test these model through tinyshakespeare data.
- implement embedding.
Ch7.Problems and solutions that can arise during learning -> code link
- Concept of overfitting and underfiting.
- Implement methods to prevent overfittig -> Regularization, Dropout, Data Augmentation, Weight Initialization, Learning rate, normalization, BatchNorm, optimizer
Ch8.Neural style Transfer -> code link
- Concept of Transfer Learning and Style Transfer.
- implement the Style Transfer.
Ch9.Auto Encoder -> code link
- implement the Auto Encoder.(simple version, convolution version)
- implement UNet for semantic segmentation.
Ch10.GAN -> code link
- Concept of GAN.
- implement the basic GAN using MNIST images.
- implement DCGAN using MNIST images.