ML Checklist
Resources for essential topics of Machine Learning and Deep learning, including Natural language processing (NLP), Computer Vision (CV), Reinforcement Learning (RL), Self-Supervised Learning (SSL), etc.
Some Great Youtubers
Resources
- A curated list of AI resources
- The Practical Guides for Large Language Models
- Large Language Model Course
- Online textbook: Deep Dive DL
- Deep Learning Book
Yes you should understand backprop
https://colab.research.google.com/drive/1WV2oi2fh9XXyldh02wupFQX0wh5ZC-z-?usp=sharing
https://docs.python.org/3/library/copy.html
Must learn
- Neural Networks: Zero to Hero
- Stanford 231n: Convolutional Neural Networks
- Youtube video
- Notes (very useful!)
- Cousera: Deep Learning Specialization
- Stanford 224n: NLP
- C230, Deep Learning 2018, by Andrew Ng
- Practical Deep Learning (Transformer and Difussion Model)
- NLP Course | For You
- Stanford CS25: Transformers United V3
- Stanford XCS224U: Natural Language Understanding
- Stanford CS234: Reinforcement Learning
- Deep Reinforcement Learning, UC Berkeley, CS285
- UvA Deep Learning Tutorials
- Tutorial6 is about Transformer.
- Transformers from Scratch
General
Stochastic Calculus and Stochastic Control, Princeton, ACM 217
Theoretical Foundations of Reinforcement Learning, University of Alberta, CMPUT 605
Stanford CS 224R: Deep Reinforcement Learning
CS221: Artificial Intelligence: Principles and Techniques
CS 224W: Machine Learning with Graphs
CS 246W: Mining Massive Datasets
Math
NLP
Probabilistic Graphical Models
RL
- Textbook: Mathmatical Foundation of Reinforcement Learning
- Online textbook: OpenAI Spinning up
- UCL Course on RL by David Silver.
- Practical RL
Others
https://web.stanford.edu/class/cs250/
https://pwn.college/
CPU-free Computing: A Vision with a Blueprint
CV
RL
https://spinningup.openai.com/en/latest/user/introduction.html
Statistics
https://openintro-ims2.netlify.app/07-model-slr
https://statproofbook.github.io/I/ToC
https://www.cs.cmu.edu/~aarti/Class/10704_Fall16/
Research
SSM
Diffusion models
- Tutorial on Diffusion Models for Imaging and Vision
- Step-by-Step Diffusion: An Elementary Tutorial
- 生成扩散模型漫谈（一）：DDPM = 拆楼 + 建楼