wingIDE 使用的字体：Simsun Courier New.ttf 默认时 使用 Courier New 字体，中文显示乱码，通过 Edit -> Preferences -> User Interface，Source Code Font/Size -> User Selected Font/Size，修改为宋体，能正常显示中文，但习惯 Courier New 字体的程序猿就郁闷了... 这个字体实际是宋体和 Courier New 的混血，虽没什么技术含量，至少比那些简单混合的字体漂亮多咧。

微软雅黑Monaco字体——最适合编程的字体； 微软雅黑Courier New字体——最适合编程的字体

一种改进的减小papr的低复杂度的pts方法

公交线路查询系统的设计与实现系统毕业设计参考,线路查询、站点查询、信息管理

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security. Table of Contents Chapter 1 Why We Are Interested In Machine Learning Chapter 2 Machine Learning, Statistics, And Data Analytics Chapter 3 Pattern Recognition Chapter 4 Neural Networks And Deep Learning Chapter 5 Learning Clusters And Recommendations Chapter 6 Learning To Take Actions Chapter 7 Where Do We Go From Here?