@cyrilXBT: Andrew Ng just taught the entire mathematical foundation of machine learning in one lecture. Free. Stanford University …

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machine-learning education stanford cs229 andrew-ng optimization-algorithms

Summary

Andrew Ng shares his Stanford CS229 lecture covering core machine learning mathematics, including locally weighted regression, maximum likelihood, logistic regression, and Newton's method, providing developers with a comprehensive guide to ML fundamentals.

Andrew Ng just taught the entire mathematical foundation of machine learning in one lecture. Free. Stanford University CS229. 4 concepts most developers spend YEARS piecing together: Locally weighted regression — fit any curve, not just lines. Maximum likelihood — why squared error actually makes sense statistically. Logistic regression — the real math behind every binary classifier you have ever used. Newton's Method — reaches the optimum in quadratic time while gradient descent is still warming up. This is the lecture that separates engineers who understand the tools from engineers who just use them. Save it. Watch it tonight.
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@AYi_AInotes: Fellow developers working on LLM production deployment, check out Andrew Ng's new course. The free version gives you access to all videos and base code. This course is not another rerun of the 'Attention is All You Need' math derivation, nor another set of mystical prompt-tuning tricks, nor yet another toy...

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Andrew Ng has launched a new course on LLM production deployment. The free version provides access to all videos and base code. The course dives deep into LLM internals, inference optimization (such as quantization, KV Cache, Flash Attention, speculative decoding), and hardware-aware optimization. Taught by AMD's VP of Engineering, it aims to help developers transform Transformer from an academic concept into a debuggable, optimizable engineering tool.