Switchmode Power Supply Handbook, Second Edition

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Over 60 hands-on recipes to help you master the power of Delphi for cross-platform and mobile development on multiple platforms. 本书包含完整的书签，方便阅读。

数据结构与算法，C描述。第二版，英文讲义PPT。 包含9章：Ch04-LISTS-STACKS-QUEUES(数据结构)，Chapter3-Stacks-and-Queues，Chapter5 Binary Trees， chapter6 General trees，chapter7 Sorting，chapter8 Primary & Secondary Storage，Chapter-9 Search，chapter11 Graph，chapter13 Advanced Trees Structures

Handbook of Fingerprint Recognition(Second edition)高清非扫描版

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本书中的技术描述、程序和计算机程序都是经过精心编写的，它们在广泛的应用中对作者很有用; 作者和编辑的《无线通信射频功率放大器,第二版,

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这是Joseph O'Rourke 1998年的著作《Computational Geometry in C》-Second Edition，介绍了图形学，机器人和工业设计领域应用的计算几何算法设计和实现，涵盖了计算几何中使用的所有基本技术：多边形三角剖分、凸包、Voronoi图，排列、几何查找、运动规划等。

A First Course in Machine Learning, Second Edition (Machine Learning & Pattern Recognition) by Simon Rogers, Mark Girolami 2016 | ISBN: 1498738486 | English | 427 pages | PDF | 161 MB "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." ―Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." ―Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." ―Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." ―David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." ―Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." ―Guangzhi Qu, Oakland University, Rochester, Michigan, USA