人脸识别python代码

说明：  目前一种快速稳健的卷积神经网络yolov3的代码，在protorch平台上运行(At present, the code of yolov3, a fast and robust convolutional neural network, runs on the platform of protorch)

该项目为基于python和opencv的人脸识别门禁系统，用了opencv的lbph算法，达到百分之七十以上的相似度才可认为是识别成功

采用多线程爬虫方式对前途无忧的招聘数据进行获取解析(Using multi thread crawler method to obtain the future worry free recruitment data analysis)

说明：  高斯二阶帕累托解的python实现方法，非常实用(Python implementation of Gauss second order Pareto solution)

说明：  径向基网络BP network and RBF radial basis function network contrast (fool tutorial, 10 seconds learning neural network). pdf(BP network and RBF radial basis function network contrast (fool tutorial, 10 seconds learning neural network). pdf)

说明：  三维mesh分割,用于解决分割问题，MeshCNN: A Network with an Edge(3D mesh segmentation)

machine learning 方面有关于The Bayes Classifier的资料，语言是python(machine learning aspects have The Bayes Classifier information about language is python)

学习sklearn和tensorflow的材料(materials for learning sklearn and tensorflow)

说明：  通过模型决定系数完成支持向量机参数优化，并建立基于大数据的支持向量机模型实现回归拟合(The parameters of SVM are optimized by the coefficient determined by the model, and the regression fitting is realized by the support vector machine model based on big data)

说明：  《Python数据挖掘入门与实践》高清中文版 本书作为数据挖掘入门读物，介绍了数据挖掘的基础知识、基本工具和实践方法，通过循序渐进地讲解算法，带你轻松踏上数据挖掘之旅。本书采用理论与实践相结合的方式，呈现了如何使用决策树和随机森林算法预测美国职业篮球联赛比赛结果，如何使用亲和性分析方法推荐电影，如何使用朴素贝叶斯算法进行社会媒体挖掘，等等。本书也涉及神经网络、深度学习、大数据处理等内容。本书面向愿意学习和尝试数据挖掘的程序员。("Python Data Mining Getting Started and Practice" HD Chinese version As a primer on data mining, this book introduces the basic knowledge, basic tools and practical methods of data mining. By step-by-step explanation of the algorithm, you can easily embark on the journey of data mining. This book uses a combination of theory and practice, showing how to use the decision tree and random forest algorithm to predict the results of the American professional basketball league game, how to use the affinity analysis method to recommend movies, how to use the naive Bayesian algorithm for social media mining ,and many more. This book also covers neural networks, deep learning, big data processing and more. This book is for programmers who are willing to learn and experiment with data mining.)