Halfish / Machine Learning Reference
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常见的机器学习参考资料,包括书籍、公开课等
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machine-learning-reference
1. 机器学习
1.1 书籍
-
PRML, Pattern Recognition and Machine Learning, Bishop 2006
- 经典书籍,不多说,不过我读了下面那本 MLaPP 入门的。
-
MLaPP, Machine Learning, A Probabilistic Perspective, Murphy 2012
- 很大,很全,但是比较难读,有机会啃下去最好,收获挺大的。
-
- 西瓜书出现之前,是中文书里唯一能看的,短小精悍
- 比西瓜书讲的深一点,但是内容稍微少一点,也没有系统,只是把很多流行的模型单独列成一章来讲。
-
- 西瓜书是国内著名的机器学习学者,南京大学周志华老师的新作
- 大概读了一半,感觉非常深入浅出,内容也很全,公式推导过程清晰,建议入门必读,后面常翻翻
1.2 公开课
-
CS229 Machine Learning Autumn 2016
- 机器学习课程,讲义很经典
- 也可以参考 Andrew Ng 早期的视频,在网易公开课可以找到
2. 自然语言处理(Natural Language Processing)
2.1 书籍
-
Foundations of Statistical Natural Language Processing
- Chris Manning, Hinrich Schütze, Stanford NLP Group
- 经典的书,有点老,讲的是 NLP 基础的知识。
-
- 宗成庆,清清华学出版社,2008年出版的第二版
- 讲中文 NLP 技术比较全面的书籍,兼具广度和深度,非常推荐。
- 因为成书较早,没有涉及深度学习的内容。
-
Speech and Language Processing
- Dan Jurafsky and James H. Martin 合著,现在有 3rd edition 的草稿
- 内容比较丰富,涉及 NLP 的方方面面;新版本主要添加了很多深度学习的东西
-
Deep Learning in Natural Language Processing
- Deng, Li, Liu, Yang
- 比较新的书,讲了 NLP 和 DL 结合的最新技术。
3. 深度学习(Deep Learning)
3.1 书籍
-
- GoodFellow, Bengio 合著的书,深入浅出
- 看后面几章 Research 感觉挺难懂,Practice 又不如看公开课,所以暂时弃坑
- 已有中文版,exacity/deeplearningbook-chinese
-
Neural Networks and Deep Learning
- 这本书讲的很基础,大致过一遍就好了
- 神经网络与深度学习
-
- 和上面的不是同一本书,这个是复旦大学的 邱锡鹏 老师的书,尚未出版
- 感觉和 Deep Learning Book 结构挺类似的。
3.2 视频
-
Deep Learning Summer School, Montreal 2016
- 暑假讲座,授课目标为研究生学生,工业界工程师,研究人员
- 不用fq,很流程,有 PPT
3.3 公开课
-
CS231n: Convolutional Neural Networks for Visual Recognition
-
CS224n: Deep Learning for Natural Language Processing
- 类似 CS231n,但是偏重自然语言处理和深度学习结合
- 主要用 tensorflow 实现代码和作业。网上应该也有很多中文资料。
4. 概率图模型(PGM, Probabilistic Graphical Models)
4.1 书籍
-
Probabilistic Graphical Models: Principles and Techniques
- 作者 Daphne Koller and Nir Friedman, MIT Press (2009)
- 概率图模型经典书籍
4.2 公开课
-
Probabilistic Graphical Models
- Koller 大神在 Coursera 开设的公开课
- 分成了三个子课程,Representation, Inference and Learning
-
Probabilistic Graphical Models
- Eric Xing, Carnegie Mellon University
- 知乎上推荐的,有视频
-
Probabilistic Graphical Models Spring 2013
- 非常好的公开课,PPT 很不错,参考了 Koller 的那本书
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