▍1. SVR
训练SVR模型做预测,可调整训练集和测试集比例及SVR参数，预测性能用MAP反映(The training SVR model can be used for prediction. The proportion of training set and test set and the parameters of SVR can be adjusted. The prediction performance can be reflected by MAP.)
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▍2. 大数据_协同过滤_梯度下降
给定10000个用户和他们对10000个电影的评价，然后通过协同过滤或梯度下降算法，用训练集训练数据，预测出用户对未看的电影的评分，并与测试集对比验证预测结果的准确性(You can learn Chinese,and read the Chinese introduction.)
▍3. GA_RBF_SVM
粒子群优化SVM参数 遗传算法优化SVM参数(Particle Swarm Optimization of SVM Parameters)
▍4. GA_RBF_SVM
说明： 粒子群优化SVM参数 遗传算法优化SVM参数(Particle Swarm Optimization of SVM Parameters)
▍5. knn_svm_tree
说明： 对于iris数据集的knn、svm、决策树算法分类的简单实现(Simple implementation of KNN, SVM and decision tree algorithm classification for iris dataset)
▍7. GeoQ_Spider
通过PYTHON编写的爬虫程序，爬取Geoq的人口密度数据(The population density data of Geoq is crawled by a crawler program written in PYTHON)
▍8. GeoQ_Spider
说明： 通过PYTHON编写的爬虫程序，爬取Geoq的人口密度数据(The population density data of Geoq is crawled by a crawler program written in PYTHON)
▍9. StockPricePrediction-master
python深度学习股票分析框架，就这么多了(python learning stock)
▍10. StockPricePrediction-master
说明： python深度学习股票分析框架，就这么多了(python learning stock)
▍11. 深度学习之TensorFlow：入门、原理与进阶实战
该压缩文件是《深度学习之Tensorflow的入门、原理及进阶实战》，里面讲述了如何搭建TensorFlow环境，并讲述了深度学习的一些理论基础知识，而且通过例子进行辅助，能更好的理解掌握。(The compressed file is "Introduction, Principle and Advanced Combat of Tensorflow for Deep Learning", which describes how to set up the TensorFlow environment, and describes some theoretical basic knowledge of deep learning, and assists with examples to better understand .)
▍12. 深度学习之TensorFlow：入门、原理与进阶实战
说明： 该压缩文件是《深度学习之Tensorflow的入门、原理及进阶实战》，里面讲述了如何搭建TensorFlow环境，并讲述了深度学习的一些理论基础知识，而且通过例子进行辅助，能更好的理解掌握。(The compressed file is "Introduction, Principle and Advanced Combat of Tensorflow for Deep Learning", which describes how to set up the TensorFlow environment, and describes some theoretical basic knowledge of deep learning, and assists with examples to better understand .)
▍13. Python-for-Finance-Second-Edition-master
Python是一种面向对象、解释型计算机程序设计语言，其应用领域非常广泛，包括数据分析、自然语言处理、机器学习、科学计算以及推荐系统构建等。 本书用Python语言来讲解算法的分析和设计。本书主要关注经典的算法，但同时会为读者理解基本算法问题和解决问题打下很好的基础。(Python is an object-oriented, interpretive computer programming language. It has a wide range of applications, including data analysis, natural language processing, machine learning, scientific computing and recommendation system construction. This book uses Python language to explain the analysis and design of algorithms. This book focuses on classical algorithms, but at the same time it will lay a good foundation for readers to understand basic algorithms and solve problems. The book consists of 11 chapters. The tree, graph, counting problem, inductive recursion, traversal, decomposition and merging, greedy algorithm, complex dependency, Dijkstra algorithm, matching and cutting problem, difficult problem and its dilution are introduced. The book has exercises and reference materials at the end of each chapter, which provides readers with more convenience for self-examination and further stu)
▍14. bostorder
此为人工智能中的简单遗传算法的实现,使用的开发工具为c#(This is the implementation of simple genetic algorithm in artificial intelligence. The development tool used is c#)
▍15. 机器学习Python程序
覆盖了基本常用的机器学习算法。包括线性回归与分类算法；决策树；多种降维算法；优化算法；强化学习等多类算法的Python代码。(It covers the commonly used machine learning algorithms. Including linear regression and classification algorithm; decision tree; a variety of dimensionality reduction algorithm; optimization algorithm; reinforcement learning and other algorithms of Python code.)
▍16. machinelearninginaction
Python机器学习及实践，第二章到第四章源码(Python machine learning and Practice)
▍18. 1
说明： GN算法是一种分裂型的社区结构发现算法。该算法根据网络中社区内部高内聚、社区之间低内聚的特点，逐步去除社区之间的边，取得相对内聚的社区结构。(GN algorithm is a divisive community structure discovery algorithm. According to the characteristics of high cohesion within the community and low cohesion between communities, the algorithm gradually removes the edges between the communities and achieves a relatively cohesive community structure.)
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