该仓库主要存储本人所阅读机器学习相关pdf书籍及其个人笔记笔记，其主要目的是提供一个在任何地点任何时候阅读的书籍都是最新的(版本控制系统)，使其类似于kindle的功能(笔记)。(本人经常阅读pdf书籍，在原始pdf书籍上做笔记，但有的的时候所阅读的pdf的笔记--不同的电脑上，不是最新的而困扰)。。。。。。。。。。。。。。ps:经本人测试，github的版本控制系统能够对pdf的笔记作出相关的版本控制
Algorithms. 0th Edition. Jeff Erickson.2018
Natural Language Processing. 0th Edition. Jacob Eisenstein. 2018
Statistical Thinking for the 21st Century. Draft. Russell A. Poldrack.12.2018
数字经济下的算法力量-阿里算法年度精选集. 阿里巴巴集团,2018
强化学习在阿里的技术演进与业务创新. 阿里巴巴集团,2018
Speech and Language Processing. 3th Edition draft.Daniel Jurafsky,James H. Martin. 2017
A course in machine learning. Hal Daumé III. 2017
Bayesian Reasoning and Machine Learning. Edition draft. David Barber.2017
Artificial inteligence. 2017
TensorFlow Machine Learning Cookbook. 2017
深度学习. 2017
阿里技术-年度精选-上. 阿里巴巴集团,2017
阿里技术-年度精选-下. 阿里巴巴集团,2017
Deep Learning. Ian Goodfellow ,Yoshua Bengio , Aaron Courville. MIT,2016
Reinforcement Learning An Introduction. 2th Edition draft. Richard S. Sutton and Andrew G. Barto. 2016
Think Data Structures Algorithms and Information Retrieval in Java. 1th Edition. Allen B. Downey. Green Tea Press,2016
神经⽹络与深度学习. Xiaohu Zhu,Freeman Zhang. 2016
Python网络数据采集. 陶俊杰,陈小莉. 人民邮电出版社出版,2016
深入理解Spark核心思想与源码分析. 耿嘉安. 机械工业出版社,2015
Think Stats Exploratory Data Analysis in Python. Allen B. Downey. Green Tea Press,2014
模式识别与机器学习. 马春鹏. 2014
Building Machine Learning Systems with Python. Willi Richert,Luis Pedro Coelho.2013 相关资源
Python For Data Analysis. Julie Steele and Meghan Blanchette. Oreilly,2013 中文版资源
数据可视化实战-使用D3设计交互式图表. 李松峰.人民邮电出版社,2013 Machine Learning A Probabilistic Perspective. Kevin P. Murphy. MIT,2012
Ensemble Methods Foundations and Algorithms. Zhi-Hua Zhou. CRC press,2012
Boosting Foundations and Algorithms. Robert E. Schapire,Yoav Freund. MIT,2012
Planning with Markov Decision Processes An AI Perspective. Mausam and Andrey Kolobov. 2012
Machine Learning The Art and Science of Algorithms that Make Sense of Data. Peter Flach.Cambridge,2012 ppt资源
Machine Learning for Hackers. Drew Conway and John Myles White. oreilly,2012
Foundations of Machine Learning. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.MIT,2012
统计学习方法. 李航. 清华大学出版社,2012
推荐系统实践. 项亮. 人民邮电出版社,2012
大数据互联网大规模数据挖掘与分布式处理. 王斌. 人民邮电出版社,2012
Think Stats Probability and Statistics for Programmers. 1th Edition. Allen B. Downey. Green Tea Press,2011
Data Mining-Practical Machine Learning Tools and Techniques. 3th Edition. Ian H. Witten,Eibe Frank,Mark A. Hall.Elsevier,2011
Adaptive Representations for Reinforcement Learning. Shimon Whiteson. 2010
Introduction to Machine Learning. 2th Edition. Ethem Alpaydın. MIT,2010
Reinforcement learning and dynamic programming using function approximators. Lucian Bus¸oniu, Robert Babuˇska, Bart De Schutter, and Damien Ernst. 2009
The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2th Edition. Springer, 2008
Neural Networks and Learning Machines. 3th Edition. Simon Haykin.2008 中文资源
Stanford University-cs229-lecture-note. Andrew Ng. 2008
Introduction to Machine Learning. Amnon Shashua. 2008
Reinforcement Learning. Cornelius Weber, Mark Elshaw and Norbert Michael Mayer. 2008
Programming Collective Intelligence. Toby Segaran. oreilly,2007
Pattern Recognition and Machine Learning. Christopher M. Bishop.Springer.2006
Gaussian Processes for Machine Learning. Carl Edward Rasmussen and Christopher K. I. Williams. MIT,2006
Information Theory, Inference, and Learning Algorithms. David J.C. MacKay. Cambridge University Press,2005
Machine Learning Neural and Statistical Classification. D. Michie, D.J. Spiegelhalter, C.C. Taylor. 1994
Pattern Classification. 2th Edition. Richard O.Duda,Peter E.Hart,David G.Stork 中文资源
All of Statistics A Concise Course in Statistical Inference. Larry Wasserman. Springer
The Top Ten Algorithms in Data Mining
机器学习-Mitchell-中文-清晰版
机器学习实战_中文版 相关资源
数据挖掘
TensorFlow 官方文档中文版 - v1.2
- 统计学习方法 该书不可编辑,望君贡献
- 重构书籍结构
- Reinforcement Learning 该书的某些页面需要重新裁剪
- 推荐系统实践. 项亮. 人民邮电出版社,2012
- Stanford University-cs229-lecture-note. Andrew Ng. 2008
- 数字经济下的算法力量-阿里算法年度精选集. 阿里巴巴集团,2018
- 模式识别与机器学习. 马春鹏. 2014
- 深度学习. 2017
- 10.3 -- 10.11 已读
- 联合概率分布和条件概率分母 架构的设计(框图)?
- 序列到向量、向量到序列、固定序列到固定序列、序列到变长序列的设计及其相关细节?(框图)
- 跳跃连接和删除连接的具体图例以及两者的区别?
- 递归神经网络的具体细节以及如何实现?
- 长期依赖的解决方案?
- 回声状态网络?
- 渗漏单元、线性自连接单元、时间尺度是什么以及图例?
- 为什么会产生LSTM、LSTM的灵感、LSTM的设计思想?
- 10.11 需要重读