机器学习中的数学
Mathematics for Machine Learning
Marc Peter Deisenroth、A. Aldo Faisal、Cheng Soon Ong · 初版 2019 年
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为什么值得读
- 系统提炼与机器学习紧密相关的核心数学知识
- 原创且严谨，不同于市面上彼此抄袭的同类书籍
- 提供丰富的外部优质课程链接以便延伸学习
理解 machine learning 所需的基础数学工具包括 linear algebra、analytic geometry、matrix decompositions、vector calculus、optimization、probability 和 statistics。这些主题传统上分散在不同课程中讲授，使得 data science 或 computer science 专业的学生以及从业人员难以高效学习这些数学知识。这本自成体系的教材弥合了数学教材与 machine learning 著作之间的鸿沟，以最低的前提知识要求引入数学概念。它利用这些概念推导出了四个核心 machine l
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《面向机器学习的数学》，作者 Marc Peter Deisenroth、A Aldo Faisal 和 Cheng Soon Ong。https://mml-book.com/
Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. https://mml-book.com/
……关于该领域的课程。要想确保自己在 ML 和数学方面的基础足够扎实，我推荐去啃这三本书：- Christopher Bishop 的《模式识别与机器学习》- Peter Deisenroth 的《面向机器学习的数学》- Courville、Bengio 和 Goodfellow 写的《深度学习》。这三本书网上都能合法找到某种形式的电子版。不过在找 ML 相关工作方面我就给不了什么经验了。
…classes on the subject. Three books that I would recommend going over to make sure your foundation in ML and mathematics are solid are -Pattern recognition and machine learning by Christopher Bishop -Mathematics for Machine Learning by Peter Deisenroth -Deep Learning by Courville, Bengio and Goodfellow All three are legally available online in some form. I can't say I have any experience in finding a job related to ML though.
……形式化。即时反馈循环有助于同时培养计算思维和数学严谨性。像 Jupyter notebook 这样的工具让整个工作流衔接得天衣无缝。如果有人在找类似的资源，Deisenroth 等人写的《面向机器学习的数学》配合 3Blue1Brown 的线性代数系列视频，把理论和计算完美连接了起来，体验绝佳。
…formally. The instant feedback loop helps develop both computational thinking and mathematical rigor. Tools like Jupyter notebooks make this workflow seamless. For anyone interested in similar resources, "Mathematics for Machine Learning" by Deisenroth et al. and 3Blue1Brown's linear algebra series complement this beautifully by bridging theory and computation.
好书。 我觉得现在正好可以提几本（相关的）好书 1. 机器学习数学的入门读物 [0]。风格比楼主这本没那么严肃。它还有一章讲概率。完全可以作为楼主所链接那本书的前传。 2. 讲通用数据科学中概率相关主题的书：高维几何、随机……
Great book. I think it's a good time to mention a couple of nice books (related) 1. Elementary intro to math of machine learning [0]. Its style is a bit less austere than that of OP's. It also has a chapter on probability. It could possible serve as a great prequel to the book linked in the OP. 2. The book on probability related topics of general data science: high-dimensional geometry, random
这本打基础：https://mml-book.github.io/ 这本讲实际方法：http://www.deeplearningbook.org/ 这本也有用，但比前两本难读：https://web.stanford.edu/~hastie/Papers/ESLII.pdf
This for the background: https://mml-book.github.io/ This for actual methods: http://www.deeplearningbook.org/ This is also useful, but harder to read than the previous ones: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
更多免费书： * 《Vectors, Matrices, and Least Squares》——在我看来是最好的、对初学者友好且侧重应用的线性代数入门（或复习）。覆盖了大量基础知识，同时保持连贯简洁。练习很多，还有一本 Julia 补充书。（http://vmls-book.stanford.edu/） * 《Mathematics for Machine Learning》——对最重要的数学概念覆盖得不错……
More free books: * Vectors, Matrices, and Least Squares — IMO the best beginner-friendly and applications-focused intro to (or review of) linear algebra. Covers a ton of fundamental ground while keeping things consistent and concise. Lots of exercises and a Julia supplement book. (http://vmls-book.stanford.edu/) * Mathematics for Machine Learning — good coverage of the most important math concep