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πŸŽ“ ML Course Notes

A place to collaborate and share course notes on all topics related to machine learning, NLP, and AI.

WIP denotes work in progress

πŸ†• denotes recently published notes

MIT 6.S191 Introduction to Deep Learning (2022)

Website | Lectures by: Alexander Amini and Ava Soleimany

Lecture Description Video Notes Author
Introduction to Deep Learning Basic fundamentals of neural networks and deep learning. Video Notes Elvis
RNNs and Transformers Introduction to recurrent neural networks and transformers. Video Notes Elvis
Deep Computer Vision Deep Neural Networks for Computer Vision. Video Notes Elvis
Deep Generative Modeling Autoencoders and GANs. Video Notes Elvis
Deep Reinforcement Learning Deep RL key concepts and DQNs. Video Notes Elvis
Limitations and New Frontiers Limitations and New Frontiers in Deep Learning. Video WIP Elvis
Autonomous Driving with LiDAR Autonomous Driving with LiDAR. Video WIP Elvis

CS224N: Natural Language Processing with Deep Learning (2022)

Website

Lecture Description Video Notes Author
Introduction and Word Vectors Introduction to NLP and Word Vectors. Video Notes πŸ†• Elvis
Neural Classifiers Neural Classifiers for NLP. Video WIP Elvis

CS25: Transformers United

Website | Instructors: Div Garg, Chetanya Rastogi, Advay Pal

Lecture Description Video Notes Author
Introduction to Transformers A short summary of attention and Transformers. Video Notes πŸ†• Elvis
Transformers in Language: GPT-3, Codex The development of GPT Models including GPT3. Video WIP Elvis

Machine Learning Specialization (2022)

Website

Lecture Description Video Notes Author
Introduction to Machine Learning Supervised Machine Learning: Regression and Classification Videos WIP Elvis
Advanced Learning Algorithms Advanced Learning Algorithms Videos WIP Elvis
Unsupervised Learning, Recommenders, Reinforcement Learning Unsupervised Learning, Recommenders, Reinforcement Learning Videos WIP Elvis

How To Contribute

  1. Identify a course and lecture from this list. If you are working on notes for a lecture, please indicate by opening an issue. This avoids duplicate work.
  2. Write your notes, preferably in a Google document, Notion document, or GitHub repo.
  3. We care about quality, so make sure to revise your notes before submitting.
  4. Once you are finished, open a PR here.

If you encounter any problems with the notes? Reach out to the authors of the notes and open issue here.

If you have any questions, open an issue or reach out to me on Twitter.

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