btobab/Machine-Learning-notes
A series of the ML formula derivation notes
This is a collection of detailed mathematical derivations for various machine learning models, presented as comprehensive notes in both English and Chinese. It provides an in-depth look at how algorithms like Linear Regression, PCA, and Naive Bayes are formulated from fundamental mathematical principles. Students, researchers, and practitioners who need to deeply understand the theoretical underpinnings of machine learning algorithms will find this useful.
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Use this if you need to understand the detailed mathematical derivations and theoretical foundations behind common machine learning algorithms.
Not ideal if you are looking for ready-to-use code implementations or a high-level conceptual overview without the mathematical detail.
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TeX
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Apache-2.0
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Dec 23, 2021
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