Lecture Videos

• Video list: http://video.tjwei.tw
• Youtube Playlist
  • English: https://www.youtube.com/playlist?list=PLl6jkZVaP8zr7bXbZ4QA4fhq0P29TqeiI
  • Chinese: https://www.youtube.com/playlist?list=PLl6jkZVaP8zpjcbMk3LZBpbrFduAmknEg

1 Introduction and Warm up

• 0 Introduction

  Introduction

• 1 Autoencoder (Homework 1)

  Autoencoder

2-3 Autoregressive models and n-gram language generation

• 2.0-2.3

  Autoregressive Models

• RNN and character-level language models

  • Understanding LSTM Networks -- colah's blog

    • Slides 2.4
    • https://docs.google.com/presentation/d/1XJXIVhdgEecHsAmIO68c4UTMrIUp539K/edit?usp=sharing&ouid=107956359237789538519&rtpof=true&sd=true

  • 3: Slides Slides 3

  • 3.1 The Unreasonable Effectiveness of Character-level Language Models

    • https://colab.research.google.com/github/norvig/pytudes/blob/main/ipynb/Goldberg.ipynb

  • 3.2 The Unreasonable Effectiveness of Recurrent Neural Networks (karpathy.github.io)

  • 3.3 PixelRNN and PixelCNN

    PixelRNN and PixelCNN

  • slides notes (lectures 5 and 6)

• Homework 2

  Homework 2

4- VAE and Generative Flow

VAE

Variational Autoencoder

VQVAE

VQVAE

Generative Flow

Generative Flow

5,6,7 Generative Adversarial Networks