编译器龙书第二版自2006年出版很久了， 但是网上的资源都是扫描版的djvu, pdf版， 看起来很不爽。 现在给大家提供这本经典书籍的高清非扫描版, true pdf. 注意：有些pdf阅读器可能不能正常显示文档内容。

达芙妮 Koller 2009年大作，不用多说，概率图模型经典，因为是从国外网站购买的高清版pdf，所以有偿分享一下。 （绝对不是扫描板！请放心下载！1270页）

数据挖掘领域里程碑意义的经典著作！不可不看！原书第三版较翻译版，表述更精准。本资源为epub格式，可以转为mobi、pdf等格式。方便在手机、kindle、pc端阅读。

D. H. Johnson and D.E. Dudgeon, Array Signal Processing: Concepts and Techniques, Prentice Hall, 1993.

Exploring an advanced state of the art deep learning models and its applications using Popular python libraries like Keras, Tensorflow, and Pytorch Key Features • A strong foundation on neural networks and deep learning with Python libraries. • Explore advanced deep learning techniques and their applications across computer vision and NLP. • Learn how a computer can navigate in complex environments with reinforcement learning. Book Description With the surge of Artificial Intelligence in each and every application catering to both business and consumer needs, Deep Learning becomes the prime need of today and future market demands. This book explores deep learning and builds a strong deep learning mindset in order to put them into use in their smart artificial intelligence projects. This second edition builds strong grounds of deep learning, deep neural networks and how to train them with high-performance algorithms and popular python frameworks. You will uncover different neural networks architectures like convolutional networks, recurrent networks, long short term memory (LSTM) and solve problems across image recognition, natural language processing, and time-series prediction. You will also explore the newly evolved area of reinforcement learning and it will help you to understand the state-of-the-art algorithms which are the main engines behind popular game Go, Atari, and Dota. By the end of the book, you will be well versed with practical deep learning knowledge and its real-world applications What you will learn • Grasp mathematical theory behind neural networks and deep learning process. • Investigate and resolve computer vision challenges using convolutional networks and capsule networks. • Solve Generative tasks using Variational Autoencoders and Generative Adversarial Nets (GANs). • Explore Reinforcement Learning and understand how agents behave in a complex environment. • Implement complex natural language processing tasks using recurrent networks (LSTM

专门讲述事务处理的、最经典的教材之一（个人认为可以去掉“之一”这两个字）。当年自己也是学的这本教材，没学好。工作好发现，类似长事务等比较炫的概念，其实大牛在很多年前就讨论过了。 书是1993年版本的，教材形式，稍偏理论，不喜勿下。

计算机动画算法的经典书籍

介绍数字接收机同步技术的权威之作，涉及时间、相位、频率同步

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems. This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

Wideband Beamforming