统计学习导论

Hacker News 上怎么说

• wenc · 2021 年

  ……这些原则。读完这本后，我开始注意到数据基础设施中的通用模式，这帮我迅速搞懂了新技术是怎么运作的。（大部分都是同一套原则的变种）* 《Introduction to Statistical Learning》（James 等人著）和《Applied Predictive Modeling》（Kuhn 等人著）。这两本书全面梳理了深度学习出现之前的预测建模方法，这些方法至今仍旧非常有用，并且适用于……

  …those principles. After reading this, I began to notice general patterns in data infrastructure, which helped me quickly grasp how new technologies worked. (most are variations on the same principles) * Introduction to Statistical Learning (James et al) and Applied Predictive Modeling (Kuhn et al). These two books gave me a grand sweep of predictive modeling methods pre-deep learning, methods which continue to be useful and applicable to…

• psv1 · 2019 年

  ……材料）。这能给你打下数学基础。还有些更偏向 ML 的书：- 《Foundations of Data Science》- Blum 等人 - 《Elements of Statistical Learning》- Hastie 等人。这本书的简化版——《Introduction to Statistical Learning》——在斯坦福官网上还有免费配套课程。- 《Machine Learning: A Probabilistic Perspective》- Murphy。要消化的内容挺多的。而且在某个节点上，你就该开始动手做实验了……

  …material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Hastie et al. The simpler version of this book - Introduction to Statistical Learning - also has a free companion course on Stanford's website. - Machine Learning: A Probabilistic Perspective - Murphy That's a lot of material to cover. And at some point you should start experimenting…

• uoaei · 2020 年

  《Introduction to Statistical Learning》https://faculty.marshall.usc.edu/gareth-james/ISL/ 《Elements of Statistical Learning》https://web.stanford.edu/~hastie/ElemStatLearn/ 《Machine Learning: A Probabilistic Perspective》https://mitpress.mit.edu/books/machine-learning-1

  Introduction to Statistical Learning https://faculty.marshall.usc.edu/gareth-james/ISL/ Elements of Statistical Learning https://web.stanford.edu/~hastie/ElemStatLearn/ Machine Learning: A Probabilistic Perspective https://mitpress.mit.edu/books/machine-learning-1

• shogunmike · 2015 年

  ……想要从更偏数学/统计的角度来切入 Machine Learning 的话，那 Hastie/Tibshirani 等人写的那两本书绝对值得一读（而且作者官网上还能免费下载！）：《Introduction to Statistical Learning》：http://www-bcf.usc.edu/~gareth/ISL/ 《The Elements of Statistical Learning》：http://statweb.stanford.edu/~tibs/ElemStatLearn/ 当然还有整个“深度学习”领域！一个不错的入门……

  …want a more mathematical/statistical take on Machine Learning, then the two books by Hastie/Tibshirani et al are definitely worth a read (plus, they're free to download from the authors' websites!): Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ The Elements of Statistical Learning: http://statweb.stanford.edu/~tibs/ElemStatLearn/ Obviously there is the whole field of "deep learning" as well! A good place…

• RogerL · 2015 年

  ……反正别自己手写数值计算代码；早就有人写过了，而且他们处理了朴素实现会遗漏的所有 edge case。你可以找到 James、Witten、Hastie、Tibshirani 写的书《An Introduction to Statistical Learning》的 PDF [1]。翻翻看吧——数学上完全没有什么吓人的。所有脏活累活全交给 R 了。放心入坑吧，水温正合适！[1] http://web.stanford.edu/~hastie/pub.htm

  …write your own numerical code anyway; someone else did it, and they handled all the edge cases that a naive implementation misses. You can find PDFs of the James, Witten, Hastie, Tibshirani book "An Introduction to Statistical Learning" [1]. Scroll on through - there is nothing intimidating math wise. All the heavy lifting is left to R. Jump in, the water is fine! [1] http://web.stanford.edu/~hastie/pub.htm

• Mxtetris · 2020 年

  James、Witten、Hastie 和 Tibshirani 所著的《An Introduction to Statistical Learning》。下载地址：http://faculty.marshall.usc.edu/gareth-james/ISL/ Taylor 和 Karlin 所著的《An Introduction to Stochastic Modeling》

  James, Witten, Hastie, and Tibshirani, "An Introduction to Statistical Learning." Available for download: http://faculty.marshall.usc.edu/gareth-james/ISL/ Taylor and Karlin, "An Introduction to Stochastic Modeling"

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