Python Deep Learning

MATLAB+R2014a完全自学一本通，电子书籍，完整的一本书，可以从其中学习matlab详细的知识(MATLAB+R2014aCompletely self-taught)

说明：  数据与计算机通信（Data and Computer Communications）答案(Data and Computer Communications Reference Answers)

C#从入门到实践（全23章），适合初学者快速了解。(C# from entry to practice (all 23 chapters))

说明：  介绍算法的书及资料，有要学习算法的宝宝可以看看。(If you want to learn programming ,you can read this book.)

说明：  本书主要针对对人工智能感兴趣，并想要零基础学习强化学习的从事机器学习开发人 员和热衷于深度学习的爱好者。(Reinforcement learning)

J.B.Rawlings and D.Q.Mayne所著的 模型预测控制的理论和设计(Model Predictive control: Theory and Design)

电脑报2003增刊(computer reported 2003 Supp)

有需要 倍福入门教程的 话，可以下载哈哈哈(You can download the Beifu introductory tutorial if you need it.)

作者: David M. Beazley / Brian K. Jones 出版社: 人民邮电出版社 原作名: Python Cookbook 3rd Edtioni 译者: 陈舸 出版年: 2015-5-1 页数: 684 定价: 108.00元 装帧: 平装 ISBN: 9787115379597 《Python Cookbook（第3版）中文版》介绍了Python应用在各个领域中的一些使用技巧和方法，其主题涵盖了数据结构和算法，字符串和文本，数字、日期和时间，迭代器和生成器，文件和I/O，数据编码与处理，函数，类与对象，元编程，模块和包，网络和Web编程，并发，实用脚本和系统管理，测试、调试以及异常，C语言扩展等。(The Python Cookbook (Version 3) Chinese version introduces some tips and methods for using Python in various fields, covering topics such as data structures and algorithms, strings and text, numbers, dates and times, iterators, and builds File and I / O; Data Encoding and Processing; Functions; Classes and Objects; Meta-programming; Modules and Packages; Web and Web Programming; Concurrency; Utility Scripting and Systems Management; Testing; Debugging; Anomalies;)

Python数据挖掘入门与实践 本书作为数据挖掘入门读物，介绍了数据挖掘的基础知识、基本工具和实践方法，通过循序渐进地讲解算法，带你轻松踏上数据挖掘之旅。本书采用理论与实践相结合的方式，呈现了如何使用决策树和随机森林算法预测美国职业篮球联赛比赛结果，如何使用亲和性分析方法推荐电影，如何使用朴素贝叶斯算法进行社会媒体挖掘，等等。本书也涉及神经网络、深度学习、大数据处理等内容。本书面向愿意学习和尝试数据挖掘的程序员。(Introduction and practice of Python data mining As a primer on data mining, this book introduces the basic knowledge, basic tools and practical methods of data mining. By step-by-step explanation of the algorithm, you can easily embark on the journey of data mining. This book uses a combination of theory and practice, showing how to use the decision tree and random forest algorithm to predict the results of the American professional basketball league game, how to use the affinity analysis method to recommend movies, how to use the naive Bayesian algorithm for social media mining ,and many more. This book also covers neural networks, deep learning, big data processing and more. This book is for programmers who are willing to learn and experiment with data mining.)