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abe4a959910d4fad10509950d60c101f0237524250d8613c5c549dd5845ad4b3GitHub description at capture
🎓 Sharing machine learning course / lecture notes.
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# 🎓 Machine Learning Course Notes
A place to collaborate and share lecture notes on all topics related to machine learning, NLP, and AI.
`WIP` denotes work in progress.
---
### Machine Learning Specialization (2022)
[Website](https://www.coursera.org/specializations/machine-learning-introduction) | Instructor: Andrew Ng
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Introduction to Machine Learning</td>
<td class="tg-yw4l">Supervised Machine Learning: Regression and Classification</td>
<td class="tg-yw4l"><a href="https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction">Videos<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Course-1-Supervised-Machine-Learning-3a200719f58145dc8a701a2545bdf9f4">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Advanced Learning Algorithms</td>
<td class="tg-yw4l">Advanced Learning Algorithms</td>
<td class="tg-yw4l"><a href="https://www.coursera.org/learn/advanced-learning-algorithms?specialization=machine-learning-introduction">Videos<a></td>
<td class="tg-yw4l">WIP</td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Unsupervised Learning, Recommenders, Reinforcement Learning</td>
<td class="tg-yw4l">Unsupervised Learning, Recommenders, Reinforcement Learning</td>
<td class="tg-yw4l"><a href="https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction">Videos<a></td>
<td class="tg-yw4l">WIP</td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### MIT 6.S191 Introduction to Deep Learning (2022)
[Website](http://introtodeeplearning.com/) | Lectures by: Alexander Amini and Ava Soleimany
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Introduction to Deep Learning</td>
<td class="tg-yw4l">Basic fundamentals of neural networks and deep learning.</td>
<td class="tg-yw4l"><a href="https://youtu.be/7sB052Pz0sQ">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-1-Intro-to-DL-d4929997a7a34a33a163cf40ba00360b">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">RNNs and Transformers</td>
<td class="tg-yw4l">Introduction to recurrent neural networks and transformers.</td>
<td class="tg-yw4l"><a href="https://youtu.be/QvkQ1B3FBqA">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-2-Recurrent-Neural-Networks-and-Transformers-71fb3ba2a24f4b6c8cc77281fc19cfab">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Deep Computer Vision</td>
<td class="tg-yw4l">Deep Neural Networks for Computer Vision.</td>
<td class="tg-yw4l"><a href="https://youtu.be/uapdILWYTzE">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-3-Deep-Computer-Vision-e43a17b50f7e4b5f8393c070b22340a3">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Deep Generative Modeling</td>
<td class="tg-yw4l">Autoencoders and GANs.</td>
<td class="tg-yw4l"><a href="https://youtu.be/QcLlc9lj2hk">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-4-Deep-Generative-Modeling-928d24a5764d4bf1bcf5fb4c4234f6ac">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Deep Reinforcement Learning</td>
<td class="tg-yw4l">Deep RL key concepts and DQNs.</td>
<td class="tg-yw4l"><a href="https://youtu.be/-WbN61qtTGQ">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-5-Deep-Reinforcement-Learning-8ecc8b16a5ad4fcc81b5c3ceb21608b5">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### CMU Neural Nets for NLP (2021)
[Website](http://phontron.com/class/nn4nlp2021/schedule.html) | Instructor: Graham Neubig
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Introduction to Simple Neural Networks for NLP</td>
<td class="tg-yw4l">Provides an introduction to neural networks for NLP covering concepts like BOW, CBOW, and Deep CBOW</td>
<td class="tg-yw4l"><a href="https://www.youtube.com/watch?v=vnx6M7N-ggs&ab_channel=GrahamNeubig">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-1-Introduction-to-Simple-Neural-Networks-for-NLP-b7afa29af56e4d47a75fbcf3b82407db">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### CS224N: Natural Language Processing with Deep Learning (2022)
[Website](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | Instructor: Christopher Manning
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Introduction and Word Vectors</td>
<td class="tg-yw4l">Introduction to NLP and Word Vectors.</td>
<td class="tg-yw4l"><a href="https://youtu.be/rmVRLeJRkl4">Video<a></td>
<td class="tg-yw4l"><a href="https://dair-ai.notion.site/Lecture-1-Introduction-and-Word-Vectors-afdc392dd83e44faab91f7c1b8f563a0">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Neural Classifiers</td>
<td class="tg-yw4l">Neural Classifiers for NLP.</td>
<td class="tg-yw4l"><a href="https://youtu.be/gqaHkPEZAew">Video<a></td>
<td class="tg-yw4l"><a href="https://github.com/dair-ai/ML-Course-Notes/issues/4">WIP</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### CS25: Transformers United
[Website](https://web.stanford.edu/class/cs25/) | Instructors: Div Garg, Chetanya Rastogi, Advay Pal
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Introduction to Transformers</td>
<td class="tg-yw4l">A short summary of attention and Transformers.</td>
<td class="tg-yw4l"><a href="https://youtu.be/P127jhj-8-Y">Video<a></td>
<td class="tg-yw4l"><a href="https://www.notion.so/dair-ai/Introduction-to-Transformers-4b869c9595b74f72b088e5f2793ece80">Notes</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Transformers in Language: GPT-3, Codex</td>
<td class="tg-yw4l">The development of GPT Models including GPT3.</td>
<td class="tg-yw4l"><a href="https://youtu.be/qGkzHFllWDY">Video<a></td>
<td class="tg-yw4l">WIP</td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### Neural Networks: Zero to Hero
[Lectures](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) | Instructors: Andrej Karpathy
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Let's build GPT: from scratch, in code, spelled out</td>
<td class="tg-yw4l">Detailed walkthrough of GPT</td>
<td class="tg-yw4l"><a href="https://youtube.com/watch?v=kCc8FmEb1nY&feature=sharesY">Video<a></td>
<td class="tg-yw4l"><a href="">WIP</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### Miscellaneous Lectures
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Lecture</b></th>
<th class="tg-yw4l"><b>Description</b></th>
<th class="tg-yw4l"><b>Video</b></th>
<th class="tg-yw4l"><b>Notes</b></th>
<th class="tg-yw4l"><b>Author</b></th>
</tr>
<tr>
<td class="tg-yw4l">Introduction to Diffusion Models</td>
<td class="tg-yw4l">Technical overview of Diffusion Models</td>
<td class="tg-yw4l"><a href="">Video<a></td>
<td class="tg-yw4l"><a href="">WIP</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
<tr>
<td class="tg-yw4l">Reinforcement Learning from Human Feedback (RLHF)</td>
<td class="tg-yw4l">Overview of RLHF</td>
<td class="tg-yw4l"><a href="">Video<a></td>
<td class="tg-yw4l"><a href="">WIP</a></td>
<td class="tg-yw4l"><a href="https://twitter.com/omarsar0">Elvis<a></td>
</tr>
</table>
---
### How To Contribute
1) Identify a course and lecture from this [list](https://github.com/dair-ai/ML-YouTube-Courses). 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 have any questions, open an issue or reach out to me on [Twitter](https://twitter.com/omarsar0).
Join our [Discord](https://discord.gg/FzNtjEK9dg).