机器学习
Machine learning
Kevin P. Murphy · 初版 2012 年
15人推荐
28位读者提及
31次提及
为什么值得读
- 以概率视角提供全面严密的理论框架
- 侧重原理理解而非单纯的代码实现
- 内容兼具前沿性与系统性
本教材基于统一的概率视角，对机器学习领域进行了全面且自成体系的介绍。内容兼具广度与深度，不仅涵盖概率论、最优化和线性代数等必备背景知识，还探讨了该领域的最新进展，包括 conditional random fields、L1 regularization 以及 deep learning。本书行文通俗易懂，并为核心算法配备了伪代码。所有主题均配有丰富的彩色图表和
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……教程，但没有一个能把完整的进阶路径讲清楚。以下是我觉得很有用的资源：========================================== 来自 Open AI、Facebook AI 大佬的建议 必刷课程：Andrew Ng 的 Machine Learning（https://www.coursera.org/learn/machine-learning）/// 课程笔记：（http://holehouse.org/mlclass/index.html）Yaser Abu-Mostafa 的 Machine Learning 课程，更侧重于理论……
…tutorials but none of it tells you the whole path. Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html) Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory…
Murphy 写的那本可能是内容最新的，而且是本非常出色的教材（去看看第一条书评吧）。另外，网上还有很多不同严谨程度的学习资料：http://www.amazon.com/Machine-Learning-Probabilistic-Perspec... http://metaoptimize.com/qa/questions/186/good-freely-availab... http://www.p-value.info/2012/11/free-datascience-books.html http://www.kaggle.com/wiki/Tutorials
Murphy's is probably most current, and an excellent text (read first review). OTW there's lots of stuff on the web for various levels of rigor http://www.amazon.com/Machine-Learning-Probabilistic-Perspec... http://metaoptimize.com/qa/questions/186/good-freely-availab... http://www.p-value.info/2012/11/free-datascience-books.html http://www.kaggle.com/wiki/Tutorials
以下是我觉得很有用的资源：========================================== 来自 Open AI、Facebook AI 大佬的建议 必刷课程：Andrew Ng 的 Machine Learning（https://www.coursera.org/learn/machine-learning）/// 课程笔记：（http://holehouse.org/mlclass/index.html）Yaser Abu-Mostafa 的 Machine Learning 课程，更侧重于理论……
Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html) Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory…
Kevin Murphy 写的《Machine Learning: A Probabilistic Perspective》里有一章专门讲这个。或者你也可以去读读 90 年代和 2000 年代初关于这方面的几篇论文。
Machine Learning: A Probabilistic Perspective by Kevin Murphy has a chapter on it. Or you can read the few papers from the 90s and early 00s on it.
我个人很喜欢的两本：1. Murphy 的《Machine Learning》，这是一部经典之作，对该领域进行了严谨的介绍。它侧重理论而非具体实现，因此更适合用来系统建立对机器学习基础原理的理解，而不是……
Two that I have enjoyed: 1. "Machine Learning" - Murphy It's a classic and a rigorous introduction to the subject. It focuses on theory over implementation so it's more for developing a principled understanding of machine learning fundamentals than…
……尽可能剥离？你是说那种极其细致耐心的阐述吗？你的意思是从第一性原理出发，从集合论逐步构建出高阶概率论、线性代数和统计学，然后再深入到机器学习中？Murphy 的《Machine Learning: A Probabilistic Perspective》足足有 1000 多页。讲机器学习背后的数学理论，这本书简直好得无可挑剔。那这本书要是按你说的写会是什么样……
…possible removed? Do you mean extremely patient exposition? Do you mean a first principles approach that builds advanced probability theory, linear algebra and statistics from set theory, then dives into machine learning? Murphy’s Machine Learning: A Probabilistic Perspective is just over 1000 pages long. It’s an exceptionally good book for the mathematical theory behind machine learning. What would this book look like…