ML-Course-Notes - 🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
Created at: 2022-03-17 19:18:50
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License: CC0-1.0
🎓 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
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 |
Elvis |
| Neural Classifiers | Neural Classifiers for NLP. | 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 |
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Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)