PMBOK中文（第五版）高清pdf版

内容提要: 本书共8章。包括前向多层人工神经网络,按照自适应谐振理论构成的自组织神经网络,自组织特征映射与联想记忆等。

作者阿兰·F·祖尔等的基于他们对应用科学家讲授统计与R的丰富经验，为读者献上了《R语言初学者指南》这本书。为了避免同时讲授R与统计的困难，统计方法保持在最低限度。《R语言初学者指南》包括如何下载与安装R，载入和处理数据，基本绘图，函数简介，高级绘图以及初学者常见的错误。这本书包括了你开始学习R时想知道的所有内容。

《游戏之旅：我的编程感悟》是一本非常有特色的计算机编程学习书籍。其特色就在于它将作者十余年来对游戏编程的所思、所感、所悟与编程理论知识相结合，褪去了纯理论的教学理念，使读者在前人的学习过程中吸取学习经验和教训，将计算机基础知识和高级编程技术不知不觉地融入自己的头脑中

《第一行代码(Android)》第2版&源码 高清pdf带书签 以及 每一章节所附示例代码

Ubuntu是一个以桌面应用为主的Linux操作系统，其名称来自非洲南部祖鲁语或豪萨语的“ubuntu”一词（译为吾帮托或乌班图），意思是“人性”、“我的存在是因为大家的存在”，是非洲传统的一种价值观，类似华人社会的“仁爱”思想。Ubuntu基于Debian发行版和GNOME桌面环境，与Debian的不同在于它每6个月会发布一个新版本。Ubuntu的目标在于为一般用户提供一个最新的、同时又相当稳定的主要由自由软件构建而成的操作系统。Ubuntu具有庞大的社区力量，用户可以方便地从社区获得帮助。

内置函数：python解释器已经为我们定义好了的函数即内置函数，我们可以拿来就用而无需事先定义；自定义函数：我们自己根据需求，事先定制好我们自己的函数来实现某种功能，如果遇到相同的情景可以直接调用

题目都比较基础，适合算法新手入门。看了之后在上上九度，机试就不成问题了

萧德云的系统辨识理论及应用，本文讲的很全，不过感觉本文偏重辨识算法，输入信号的设计、最优实验设计没讲，不得不说有点缺陷，其次，感觉文笔一般，没有太让人想看的欲望，丁锋出了更全的系统辨识，不过暂时没搞到全的，好像也只出了第一、三册。 由于文件大，分1,2两部分。

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. 　　This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. 　　The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.