In this repository, you will find several machine learning tutorials. This repository aims to serve as a place for people who want to learn the machine learning models and code them from scratch. Mainly, the tutorials will be written in .ipynb or .py format. If you have problem on rendering the .ipynb file on GitHub, please copy the URL and paste it to nbviwer. Then, you should be able to view the notebook.
- 00: Linear Regression (code from scratch without any open source library)
- 01: Binary Classification (Generative v.s. Discriminative Model)
- 02: Convolutional Neural Network (CNN)
- 03: Recurrent Neural Network (RNN)
- 04: Explainable AI
- 05: Adversarial Attack
- 06: Network Compression (under progress)
- 07: Sequence-to-sequence Model
- 08: Clustering
- 09: Anomaly Detection (under progress)
- 10: DCGAN
- 11: Transfer Learning
- 12: Meta Learning
- 13: Life-long Learning
- 14: Reinforcement Learning (under progress)