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实用机器学习
作者：孙亮 、黄倩
出版社：人民邮电出版社
ISBN：9787115446466
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Machine learning A Probabilistic Perspective 评分:
Kevin Murphy 关于机器学习的新书，偏贝叶斯，不过内容比较前沿。 Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package–PMTK (probabilistic modeling toolkit)–that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. 作者：Kevin p. Murphy 出版日期：August 24, 2012 页数：1104 ISBN：978-0262018029
上传时间：2018-02 大小：22.67MB
22.91MB《Machine Learning A Probabilistic Perspective》
2018-06-08机器学习非常推荐的一本书，能够兼顾深度和广度、并且比较适合拥有计算机科学或其它相关领域基础的初学者的教材
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Machine Learning A Probabilistic Perspective
2017-11-02Machine Learning 高清 英文版 2012 亚马逊评论： The closest contender to this book I believe is BRML. Both are excellent textbooks and have accompanying source code. BRML is more accessible, has a free PDF version, and a stronger focus on graphical models. MLAPP has all the qualities of an excellent graduate textbook (unified presentation, valuable learning aids), and yet is unafraid of discussing detail points (e.g. omnipresent results on complexity), as well as advanced and research topics (LDA, L1 regularization).
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machine learning a probabilistic perspective
2019-04-19《机器学习：概率论视角》这本书是由Kevin P. Murphy撰写的一部经典的机器学习参考书籍。它以概率论为基础，全面而深入地介绍机器学习领域，旨在为读者提供一个统一的概率框架。该书不仅覆盖了机器学习的基础知识，...
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2016-07-17作者在内容的覆盖上兼顾了广度和深度，既提供了概率论、优化方法和线性代数等必要的基础知识，也讨论了条件随机场（Conditional Random Fields）、L1正则化（L1 Regularization）和深度学习（Deep Learning）等领域...
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2013-12-28经典的电子书籍，有时间可以看看，英文慢慢看反复看，还是能看懂的。
