Learning Bayesian Networks - Neapolitan R. E..pdf Learning Bayesian Networks - Neapolitan R. E..pdf Learning Bayesian Networks - Neapolitan R. E..pdf Learning Bayesian Networks - Neapolitan R. E..pdf

贝叶斯网络经典教材都涵盖在这里面了，欢迎大家使用~！

Computer Networks Andrew Tanenbaum 英文版第五版 pdf文字版

Wei Ren和Yongcan Cao关于多智能体系统分布式协调控制方向的经典教材。

关于这本书的介绍可以在网上找，书中第二章关于面向对象编程的东西没有扫描，相关内容可以去看更详细的书籍。 添加了书签。 关于资源分：没办法，我也要去下载别人的资源。 共有3个rar。

共3个rar，必须一起下载！没办法，上传限制呀！！说明见part1.

共3个rar，必须一起下载！说明见part1

使用神经网络进行预测，有BF，FF，GRNN，RBF网络等，

For those entering the field of artificial neural networks, there has been an acute need for an authoritative textbook that explains the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. Some authors have failed to separate the basic ideas and principles from the soft and fuzzy intuitions that led to some of the models as well as to most of the exaggerated claims. Others have been unwilling to use the basic mathematical tools that are essential for a rigorous understanding of the material. Yet others have tried to cover too many different kinds of neural network without going into enough depth on any one of them. The most successful attempt to date has been "Introduction to the Theory of Neural Computation" by Hertz, Krogh and Palmer. Unfortunately, this book started life as a graduate course in statistical physics and it shows. So despite its many admirable qualities it is not ideal as a general textbook.

by Maarten van Steen (Author) ============================= This book aims to explain the basics of graph theory that are needed at an introductory level for students in computer or information sciences. To motivate students and to show that even these basic notions can be extremely useful, the book also aims to provide an introduction to the modern field of network science. Mathematics is often unnecessarily difficult for students, at times even intimidating. For this reason, explicit attention is paid in the first chapters to mathematical notations and proof techniques, emphasizing that the notations form the biggest obstacle, not the mathematical concepts themselves. This approach allows to gradually prepare students for using tools that are necessary to put graph theory to work: complex networks. In the second part of the book the student learns about random networks, small worlds, the structure of the Internet and the Web, peer-to-peer systems, and social networks. Again, everything is discussed at an elementary level, but such that in the end students indeed have the feeling that they: 1.Have learned how to read and understand the basic mathematics related to graph theory.