@cleardusk 2015-11-27T13:22:26.000000Z 字数 3134 阅读 1135

# 学习记录.2015.11.27

GjzCV

# Deep Learning

Ian Goodfellow, Aaron Courville, Yoshua Bengio 三人合作的这本书，该书平均以每天两至三页的速度更新。该书分为三个部分，第一部分为 Applied Math and Machine Learning Basics，数学部分有 Linear Algebra, Probability and Information Theory, Numerical Computation, 剩下的就是 Machine Learning Basics 了；第二部分是 Deep Networks : Modern Practices

《费曼物理学讲义》
《费曼传》
《别闹了，费曼先生》
《普林斯顿数学指南》

# p.s.

Advice to students: if you are an undergrad, take as many math and physics course as you can, and learn to program. If you are an aspiring grad student: apply to schools where there is someone you want to work with. It's much more important that the ranking of the school (as long as the school is in the top 50). If your background is engineering, physics, or math, not CS, don't be scared. You can probably survive qualifiers in a CS PhD program. Also, a number of PhD programs in data science will be popping up in the next couple of years. These will be very welcoming to students with a math/physics/engineering background (who know continuous math), more welcoming than CS PhD programs.

Another advice: read, learn from on-line material, try things for yourself. As Feynman said: don't read everything about a topic before starting to work on it. Think about the problem for yourself, figure out what's important, then read the literature. This will allow you to interpret the literature and tell what's good from what's bad.

Physics is about modeling actual systems and processes. It's grounded in the real world. You have to figure out what's important, know what to ignore, and know how to approximate. These are skills you need to conceptualize, model, and analyze ML models.

Another set of courses that are relevant is signal processing, optimization, and control/system theory.

That said, taking math and statistics courses is good too.

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