发表在CVPR2008年，使用梯度场先验超分辨率的代码，欢迎各位下载！

Deep Learning using Linear Support Vector Machines的简单实现代码

非常经典的雷达系统分析和设计的资料，有需要的可以看看

This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own machine learning projects. Gradient Boosting does have a some fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects to deliver real value. From the applied perspective, gradient boosting is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions.

作者以一个使用者的身份介绍如何开发验证环境，里面列出了很多的规则和方法，国内有中文版本的。个人感觉还是英文的描述比较好...

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets. 通过数据科学学习数据科学！ 数据科学使用Python和R将使您进入世界上两个最广泛的数据科学开源平台：Python和R. 数据科学很热门。 Bloomberg称数据科学家是“美国最热门的工作。”Python和R是世界上最畅销的两个开源数据科学工具。在使用Python和R的数据科学中，您将逐步学习如何使用最先进的技术为现实世界

Kalman Filtering - Theory and Practice Using MATLAB第四版，有完整的目录书签

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

Silage, Dennis. Digital communication systems using SystemVue / Dennis Silage. p. cm. Includes index. ISBN 1-58450-850-7 (alk. paper) 1. Digital communications. I. Title. TK5103.7.S49 2005 621.382--dc22 2005031785

基于haar小波变换的模糊检测算法 2004 Blur Detection for Digital Images Using Wavelet Transform