JongBeomBaek/Deep-Learning-PyTorch-Study

---

Contents

---

01. Basic

• Python
• PyTorch Tensor Usage
• Linear & Logistic Regression
• Neural Network with MNIST

02. CNN

• Basic CNN with MNIST
• Basic CNN with CIFAR-10

03. RNN

• Basic RNN with MNIST
• Gated Recurrent Unit
• LSTM

04. Autoencoder

• Basic Autoencoder
• Denoising Autoencoder
• Variational Autoencoder
• Convolutional Autoencoder

05. Generative Adversarial Network

• Vanilla GAN
• LSGAN
• DCGAN

---

Requirements

---

• python 3.6.5

• PyTorch (http://pytorch.org/)

• Numpy

• matplotlib

• CUDA 10.0 (https://developer.nvidia.com/cuda-90-download-archive)

• CuDNN 7.4.2 (https://developer.nvidia.com/cudnn)

• Aanaconda (https://www.anaconda.com/download/)

• Jupyter Lab (https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html)

• Jupyter Notebook