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.

获得Palo alto Networks ACE Certification资格认证工程师，可以让您增加求职砝码，获得与自身技术水平相符的技术岗位，轻松跨入IT白领阶层拿取高薪。 Passing the ACE exam indicates that you possess the basic knowledge to successfully configure Palo Alto Networks firewalls using PAN-OS. The exam also serves as a study aid to prepare for PCNSE certification. Exam Format The ACE exam is web-based and consists of 40-50 multiple-choice questions. The exam is not timed, and you can retake it as many times as necessary to earn a passing score.

Computer Networks 英文第5版。

该书描述了网络页面链接之间的复杂系统，非常不错的经典书籍。

The wake-sleep algorithm for unsupervised neural networks 作者Hinton，提出Helmholtz机和wake-sleep算法

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence，非常有用的资料

Juniper Networks Network Connect 7.1.0.17943_x64 请大家注意，这个版本并不是越高越好，主要还是要看服务器上的配置。 我这里是Win7 x64 测试通过的

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on an unprecedented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and central developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analyzing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms; mathematical models of networks, including random graph models and generative models; and theories of dynamical processes taking place on networks. 包括计算机网络，社交网络和生物网络在内的网络研究在过去几年引起了极大的兴趣。互联网的兴起和廉价计算机的广泛应用使得以前所未有的规模收集和分析网络数据成为可能，新理论工具的开发使我们能够从多种不同类型的网络中提取知识。网络研究具有广泛的跨学科性，在许多领域都有发展，包括数学，物理，计算机和信息科学，生物学和社会科学。本书汇集了这些领域中最重要的突破，并以连贯的方式呈现，凸显了不同领域工作之间的紧密联系。 涵盖的主题包括网络测量;分析网络数据的方法，包括在物理学，统计学和社会学中开发的方法;图论的基本原理;计算机算法;网络的数学模型，包括随机图模型和生成模型;和网络上发生的动态过程理论。

神经网络与机器学习 在matlab中实现 英文原版 书中含有代码 指的参考