一本学UVM很好的，我自己就是看着这本书入门的， 希望对大家有帮助。

Code Complete - A Practical Handbook of Software Construction(2nd Ed)(919s)

Fundamentals of Kalman Filtering A Practical Approach Third Edition.pdf 是我从网上找的一本关于卡尔曼滤波的教材

Practical Optimization: Algorithms and Engineering Applications provides a hands-on treatment of the subject of optimization. A comprehensive set of problems and exercises makes the book suitable for use in one or two semesters of a first-year graduate course or an advanced undergraduate course. Each half of the book contains a full semester’s worth of complimentary yet stand-alone material. The practical orientation of the topics chosen and a wealth of useful examples also make the book suitable as a reference work for practitioners in the field. Advancements in the efficiency of digital computers and the evolution of reliable software for numerical computation during the past three decades have led to a rapid growth in the theory, methods, and algorithms of numerical optimization. This body of knowledge has motivated widespread applications of optimization methods in many disciplines, e.g., engineering, business, and science, and has subsequently led to problem solutions that were considered intractable not too long ago.

超级赞的一本书，《Practical MMIC Design》，作者Steve Marsh，原先在英国Bookham公司做MMIC很多年。简单介绍一下本书的主要内容： Chapter 1: Introduction Chapter 2: Component Technology and Foundry Choice Chapter 3: Foundry Use and Economics Chapter 4: Simulation and Component Models Chapter 5: Design Chapter 6: Layout Chapter 7: Processing Technology Chapter 8: Test 可以看到，基本上是面面俱到，凡是做MMIC所要涉及的问题，这本书都会覆盖到。工艺、器件、设计仿真、版图及测试，甚至使用Foundry的流程，最赞的是书中配有大量的图例，表达直观，非常适合工程师阅读。

Machine Learning with Python Cookbook Practical Solutions from Preprocessing to Deep Learning 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

英文。2018版本。The authors of this book have leveraged their hands-on experience with solving real-world problems using Python and its Machine Learning ecosystem to help the readers gain the solid knowledge needed to apply essential concepts, methodologies, tools, and techniques for solving their own real-world problems and use-cases. Practical Machine Learning with Python aims to cater to readers with varying skill levels ranging from beginners to experts and enable them in structuring and building practical Machine Learning solutions.

Over the past few decades, mathematical models have become an increasingly important tool for Earth scientists to understand and make predictions about how our planet functions and evolves through time and space. These models often consist of partial differential equations (PDEs) that are discretized with a numerical method and solved on a computer.Themost commonly used discretization methods are the finite difference method (FDM), the finite volume method, the finite element method (FEM), the discrete element method, the boundary element method, and various spectral methods.

Practical Game AI Programming. create game ai and implement cutting edge ai algorithms from scratch

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techniques for predicting which categories a record belongs to; Statistical machine learning methods that "learn" from data; Unsupervised learning methods for extracting meaning from unlabeled data.