This repository holds source codes of machine learning and modulized neural networks implemented with Pytorch.
Any comments or feedbacks are welcomed, email me at imhgchoi@korea.ac.kr
- Gradient Descent : Not Pytorch -- simple gradient descent with several different conditions.
- Logistic Regression : Not Pytorch
- Deep Neural Networks : predicting handwritten numbers with MNIST dataset
- Convolutional Neural Networks : predicting handwritten numbers with MNIST dataset
- Recurrent Neural Networks : predicting future stock price trend with RNN(LSTM cells)
- AutoEncoders
6.1 Feed Forward AutoEncoder : regenerating MNIST images with a feed forward AutoEncoder
6.2 Convolutional AutoEncoder : regenerating MNIST images with a convolutional AutoEncoder
6.3 Beta-Variational AutoEncoder : regenerating MNIST images with a Beta-Variational AutoEncoder
I found it hard to build a vanilla VAE. So I adopted the Beta-VAE with an incremental Beta to help convergence.
6.4 Sparse AutoEncoder : regenerating MNIST images with a sparse AutoEncoder with 1300 hidden code units.
6.5 Denoising AutoEncoder : regenerating MNIST images that has gaussian noise with a denoising AutoEncoder. - Deep Q Network
7.1 Feed Forward DQN : training Cartpole with an RL feed forward DQN
7.2 Convolutional DQN : training Cartpole with an RL Convolutional DQN. Referenced here, but failed to master the game