▍1. XGBoost
说明： 使用XGB算法对旅客航空信息进行特征筛选，分析，分类处理。(The XGB algorithm is used for feature screening, analysis and classification of passenger aviation information.)
更多选项
▍2. XGB选择重要特征
说明： 使用XGb进行特征筛选。特征重要性，特征打分。(Use XGB for feature filtering.python)
▍3. LSTM sequence
说明： LSTM-Neural-Network-for-Time-Series-Prediction-master
▍4. CNN -- 基于tensorflow的LeNet-5实现
1. eclipse上的python项目 2. 基于tensorflow，实现了LeNet-5网络模型，包括对MNIST数据集的预处理、模型搭建和对模型的训练、验证
▍5. GMM(1)
python实现GMM算法，并使用NMI进行算法评估(Python implementation of GMM algorithm)
▍6. 洛阳理工学院健康打卡系统自动上报程序
洛阳理工学院 “健康状况管控平台” 每日自动上报程序。本程序上报的信息均为上次用户自己上报的信息(包括体温)!
▍7. 决策树三种经典算法实现
python实现决策树分类的三种经典算法(Python realizes three classical arithmetic of decision tree classification)
▍8. 决策树三种经典算法实现
说明： python实现决策树分类的三种经典算法(Python realizes three classical arithmetic of decision tree classification)
▍9. Fisher分类器
说明： Fisher线性判别器实现两类数据分类。(Fisher linear discriminant implements two kinds of data classification.)
▍10. Python深度学习
说明： 本书由Keras之父、现任Google人工智能研究员的弗朗索瓦·肖菜（Francois Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，涉及计算机视觉、自然语言处理、生成式模型等应用。 书中包含30多个代码示例，步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化，读者无须具备机器学习相关背景知识即可展开阅读。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。 本拐适合从事大数据及机器学习领域工作，并对深度学习感兴趣的各类读者。(This book is written by Francois Chollet, the father of keras and currently a researcher of Google artificial intelligence. It introduces in detail the exploration and practice of deep learning with Python and keras, involving computer vision, natural language processing, generative model and other applications. The book contains more than 30 code examples, the steps to explain in detail. Because this book is based on the accessibility and popularity of artificial intelligence, readers can read without the background knowledge of machine learning. After learning this book, readers will have the ability to build their own deep learning environment, establish image recognition model, and generate images and characters. It is suitable for all kinds of readers who are engaged in big data and machine learning and are interested in deep learning.)
▍11. Experiment1
说明： 用随机森林预测泰坦尼克号乘客生还情况的简单python程序(Random forest prediction)
▍12. xgboost算法
说明： 利用xgboost对多变量结果进行预测分析的学习，建立模型(Using xgboost to study the prediction and analysis of multivariate results and build a model)
▍13. picture
说明： 详细编写如何让使用cnn识别自己的图片，自己建立cnn模型，可修改(Write in detail how to use CNN to identify their own pictures)
▍14. IxiguaSpider
说明： 西瓜视频爬虫，难点主要获取videoid，signature（这个跟今日头条一致）,其他的就是json数据抓包的问题了。(Watermelon video crawler, the main difficulty is to obtain videoid, Signature (which is consistent with today's headline), and the other is the problem of CAPTURING JSON data package.)
▍15. imagefusion_densefuse-master
说明： 图像融合+深度学习，里面的网络结构可以根据自己的设计进行优化，总体来说还是非常不错的。(Image fusion + deep learnng)
▍16. 深度学习mtcnn
说明： 用市面上的摄像头，可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and network layers, so that each layer can learn more features and greatly improve the generalization ability. The network is deeper, the amount of calculation in each layer is reduced, and the ability of feature extraction is strengthened, so as to improve the accuracy of target classification. On the LFW data set, the accuracy of face recognition reaches 98.40%. In this experiment, mtcnn is introduced into the face detection algorithm. Its backbone network is divided into three convolutional neural networks: p-net, R-Net and o-net. Among them, o-net is the most strict in screening candidate face frames. It will output the coordinates of a human face detection frame and five facial feature points (left eye, right eye, nose, left mouth corner, right mouth corner).)
▍17. dbn-master
深度置信网络DBN，深度学习，神经网络，分类(deep belief network(DBN), deep learning, neural network, classification)
▍18. 时域卷积网络（TCN）案例模型
说明： 使用卷积神经网络处理时间序列，属于最新的处理模型，非常适合处理时间序列(The convolution neural network is the latest processing model, which is very suitable for processing time series)
▍19. ABAQUS施加周期性边界条件的插件EasyPBC V.1.3
一款简单的ABAQUS施加周期性边界条件的插件(A Simple ABAQUS Plug-in with Periodic Boundary Conditions)
▍20. EasyPBC V.1.3
说明： 一款简单的ABAQUS施加周期性边界条件的插件(A Simple ABAQUS Plug-in with Periodic Boundary Conditions)
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