线性规划与应用，英文版

优化问题的一本好书，适合通信领域的工程师或是通信专业的学生参考，可谓红宝书

文件包含了ANSYS_Fluent_Theory_Guide19.2 ANSYS_Fluent_UDF_Manual19.2 ANSYS_Fluent_Users_Guide19.2 均为最新版本

完整版，里面是扫描的，还算清楚。资源中还包括书中的代码。

来自大牛的控制理论经典书籍。 通过具体的现实例子来解释控制理论，以及一些基本的控制思想，对于自动化专业的学生十分有用，可以帮助对学科的理解。

Theory and design of microwave filters

引文原版论文，详细讲述了Ad Hoc无线网络的四种路由算法，并进行了详细的分析和比较，对于学习无线网络的同学们来说是很好的资料

比较基本的范畴论读物，适合自学。 This text and reference book on Category Theory, a branch of abstract algebra, is aimed not only at students of Mathematics, but also researchers and students of Computer Science, Logic, Linguistics, Cognitive Science, Philosophy, and any of the other fields that now make use of it.

Data Flow Analysis Theory and Practice电子书，经典书籍

One of the most exciting recent developments in machine learning is the discovery and elaboration of kernel methods for classification and regression. These algorithms combine three important ideas into a very successful whole. From mathematical programming, they exploit quadratic programming algorithms for convex optimization; from mathematical analysis, they borrow the idea of kernel representations; and from machine learning theory, they adopt the objective of finding the maximum-margin classifier. After the initial development of support vector machines, there has been an explosion of kernel-based methods. Ralf Herbrich’s Learning Kernel Classifiers is an authoritative treatment of support vector machines and related kernel classification and regression methods. The book examines these methods both from an algorithmic perspective and from the point of view of learning theory. The book’s extensive appendices provide pseudo-code for all of the algorithms and proofs for all of the theoretical results. The outcome is a volume that will be a valuable classroom textbook as well as a reference for researchers in this exciting area. The goal of building systems that can adapt to their environment and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. Recently, several research communities have begun to converge on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. TheMIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high quality research and innovative applications. Thomas Dietterich