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说明：  Python与开源GIS——数据处理、空间分析与地图制图(Python and open source GIS: data processing, spatial analysis and Cartography)

python 3.x 实现希尔伯特黄变换，其中包含了经验模态分解函数，hilbert谱分析函数，和瞬时频率计算等函数。(python 3.x implements a Hilbert yellow transform that contains functions such as empirical modal decomposition functions, hilbert spectral analysis functions, and instantaneous frequency calculations.)

说明：  Installing the pip in the python on windows platform

说明：  讲述了统计学的本质，系统的讲述了统计的基本应用与知识(This paper describes the essence of statistics, and systematically describes the basic application and knowledge of statistics)

Python Telegram Example

自己编的一个很简单的设计游戏,可以射击随机目标，随机障碍物，人物移动等(I write very easy code，shot random target,random barrir,)

作者: David M. Beazley / Brian K. Jones 出版社: 人民邮电出版社 原作名: Python Cookbook 3rd Edtioni 译者: 陈舸 出版年: 2015-5-1 页数: 684 定价: 108.00元 装帧: 平装 ISBN: 9787115379597 《Python Cookbook（第3版）中文版》介绍了Python应用在各个领域中的一些使用技巧和方法，其主题涵盖了数据结构和算法，字符串和文本，数字、日期和时间，迭代器和生成器，文件和I/O，数据编码与处理，函数，类与对象，元编程，模块和包，网络和Web编程，并发，实用脚本和系统管理，测试、调试以及异常，C语言扩展等。(The Python Cookbook (Version 3) Chinese version introduces some tips and methods for using Python in various fields, covering topics such as data structures and algorithms, strings and text, numbers, dates and times, iterators, and builds File and I / O; Data Encoding and Processing; Functions; Classes and Objects; Meta-programming; Modules and Packages; Web and Web Programming; Concurrency; Utility Scripting and Systems Management; Testing; Debugging; Anomalies;)

说明：  强化学习可以按照方法学习策略来划分成基于值和基于策略两种。而在深度强化学习领域将深度学习与基于值的Q-Learning算法相结合产生了DQN算法，通过经验回放池与目标网络成功的将深度学习算法引入了强化学习算法。(Reinforcement learning can be divided into value-based learning and strategy based learning according to method learning strategies. In the field of deep reinforcement learning, dqn algorithm is generated by combining deep learning with value-based Q-learning algorithm. Through experience playback pool and target network, deep learning algorithm is successfully introduced into reinforcement learning algorithm.)

说明：  吴恩达深度学习第一课第二周编程作业，供大家借鉴共享(DeepLearning Lesson 1, Week 2 Programming Assignments)

用BP神经网络进行函数拟合，选用了三层前馈网络，使用BP算法对参数权值进行修正，得到的结果能够在点精度上达到99.99%，总精确度可以通过调节程序中的参数进行修改