神经网络是一种模拟人脑的神经网络，以期能够实现类人工智能的机器学习
技术。
本书揭示神经网络背后的概念，并介绍如何通过Python实现神经网络。全书
分为3章和两个附录。第1章介绍了神经网络中所用到的数学思想。第2章介绍使
用Python实现神经网络，识别手写数字，并测试神经网络的性能。第3章带领读
者进一步了解简单的神经网络，观察已受训练的神经网络内部，尝试进一步改善
神经网络的性能，并加深对相关知识的理解。附录分别介绍了所需的微积分知识
和树莓派知识。
本书适合想要从事神经网络研究和探索的读者学习参考，也适合对人工智
能、机器学习和深度学习等相关领域感兴趣的读者阅读。

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis , Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
点击链接进入中文版：
时间序列分析:预测与控制

本书由系统架构领域3位领军人物亲笔撰写，该领域资深专家Norman R. Augustine作序推荐，Amazon全五星评价。
全书共分四部分。
第一部分（第1~3章）的重点是引出系统架构。第1章通过一些范例来展示架构理念，指出良好的架构，并给出本书的概要；第2章列出进行系统分析必备的思路；第3章给出分析系统架构所用的思维模式。
第二部分（第4~8章）着重对架构进行分析。第4章讨论系统的形式；第5章讨论系统的功能；第6章讲解形式与功能之间的映射，并以此给出系统架构的定义；第7章研究如何从独立于解决方案的功能陈述中衍生出系统；第8章演示怎样把这些概念汇聚成一套架构。
第三部分（第9~13章）讲解如何为复杂的系统定义架构。第9章从任务和可交付成果这两方面来概述架构师的职责；第10章探讨如何把组织机构方面的接口当成在架构中减少歧义的契机；第11章讲述如何用系统化的方式来捕获利益相关者的需求，并把它们转换成系统目标；第12章提出一些能够帮助架构师更有创意地构思并选择概念的手段；第13章讲述在开发系统时管理复杂度的一些办法。
第四部分（第14~16章）探寻帮助架构师做决策的各种计算方法及工具所具备的潜力。第14章把系统架构的过程当成一种决策制定的过程来进行讲解；第15章讲解如何对架构权衡空间中的信息进行综合；第16章演示怎样把架构决策编码成一套模型，使计算机可以根据该模型自动生成权衡空间并对其进行探索。

撬开你脑子里的那些困惑，让你重新认识游戏设计的真谛，人人都可以成为成功的游戏设计者!从更多的角度去审视你的游戏，从不完美的想法中跳脱出来，从枯燥的游戏设计理论中发现理论也可以这样好玩。本书主要内容包括：游戏的体验、构成游戏的元素、元素支撑的主题、游戏的改进、游戏机制、游戏中的角色、游戏设计团队、如何开发好的游戏、如何推销游戏、设计者的责任等。
本书适合任何游戏设计平台的游戏设计从业人员或即将从业人员，甚至游戏玩家。

Describes basic programming principles and their step-by- step applications.Numerous examples are included.

《研究之美》是计算机科学大师、“算法分析之父”高德纳（Donald E.Knuth）在20世纪70年代旅居挪威时撰写的适用于计算机科学的一种全新基础数学结构的情景小品。全书以一对追求自由精神生活的青年男女为主人公，展开了一段对于该种全新结构的发现和构造的对白。在此过程中，本书充分展示了计算机科学的从业人员进行全新领域探索时所必备的怀疑、立论、构造、证明、归纳、演绎等逻辑推理和深入反思的能力。《研究之美》可以看作是读懂高德纳的艰深著作《计算机程序设计艺术》和《具体数学》的钥匙。

1936年，24岁的图灵发表了现代计算领域奠基性的论文《论可计算数及其在判定问题上的应用》。这篇论文堪称图灵一生中最重要的贡献。然而，大众对图灵的了解多停留在破解德国的著名密码系统Enigma，帮助盟军取得二战的胜利上。对于数学家图灵，人们往往知之甚少。
在本书中，作者深入分析了图灵的这篇论文，读者只需具备高中水平的数学知识，即可轻松读懂这篇划时代的论文，了解其对现代计算发展的杰出贡献。正如人工智能之父马文·明斯基所说，图灵的论文有着超乎寻常的简洁性及数学之美。任何希望深入了解图灵及其工作的读者都不该错过这本书！

《视觉SLAM十四讲：从理论到实践》系统介绍了视觉SLAM（同时定位与地图构建）所需的基本知识与核心算法，既包括数学理论基础，如三维空间的刚体运动、非线性优化，又包括计算机视觉的算法实现，例如多视图几何、回环检测等。此外，还提供了大量的实例代码供读者学习研究，从而更深入地掌握这些内容。
《视觉SLAM十四讲：从理论到实践》可以作为对SLAM 感兴趣的研究人员的入门自学材料，也可以作为SLAM 相关的高校本科生或研究生课程教材使用。

陈泽、占海明编著的《详解MATLAB在科学计算中的应用》结合高等院
校数学课程教学和工程科学计算应用的需要，从实用角度出发，通过大量
的算法实现，详尽系统地介绍了经典数值分析的全部内容，包括非线性、
线性方程(组)的求解插值，函数逼近与数据拟合，数值积分与数值微分，
微分方程问题的求解，数值模拟等。MATLAB是贯穿本书始终的计算软件，
对书中所有的算法都给出了MATLAB程序或MATLAB函数，并讲解了大量的应
用实例，供读者参考。
《详解MATLAB在科学计算中的应用》取材新颖，叙述清晰，重点突出
，重应用而轻推导，随书光盘中附有全部案例的源代码，并有大量教学视
频，方便读者学习与提高。
本书可以作为高等院校数学、计算机、物理及工程相关专业数值分析
课程的教学参考书，也可以作为MATLAB数学实验、建模方面的参考用书，
还可以作为需要应用数值计算工作者的参考用书。

《支撑处理器的技术:永无止境地追求速度的世界》用通俗易懂的语言和大量的插图，介绍了处理器的历史、基本结构、实现原理等，还对时下流行的虚拟化技术、多任务、多核心、GPGPU等进行了全面的讲解，并介绍了有效利用处理器的各种功能来提高应用程序性能的方法。《支撑处理器的技术:永无止境地追求速度的世界》最后还介绍了处理器在移动设备、汽车、家电等方面的应用，并展望处理器的未来发展趋势，希望能对相关软硬件的开发者有所帮助。

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

这是一本程序员面试宝典！书中对IT名企代码面试各类题目的最优解进行了总结，并提供了相关代码实现。针对当前程序员面试缺乏权威题目汇总这一痛点，本书选取将近200道真实出现过的经典代码面试题，帮助广大程序员的面试准备做到万无一失。“刷”完本书后，你就是“题王”！__eol__本书采用题目+解答的方式组织内容，并把面试题类型相近或者解法相近的题目尽量放在一起，读者在学习本书时很容易看出面试题解法之间的联系，使知识的学习避免碎片化。书中将所有的面试题从难到易依次分为“将、校、尉、士”四个档次，方便读者有针对性地选择“刷”题。本书所收录的所有面试题都给出了最优解讲解和代码实现，并且提供了一些普通解法和最优解法的运行时间对比，让读者真切地感受到最优解的魅力！__eol__本书中的题目全面且经典，更重要的是，书中收录了大量独家题目和最优解分析，这些内容源自笔者多年来“死磕自己”的深入思考。__eol__码农们，你们做好准备在IT名企的面试中脱颖而出、一举成名了吗？这本书就是你应该拥有的“神兵利器”。当然，对需要提升算法和数据结构等方面能力的程序员而言，本书的价值也是显而易见的。

This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively.An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Author's website.