▍1. PSO SVR-master
演示如何在支持向量机中使用粒子群优化，以找到更好的特征并基于交叉验证精度 python中的pso-svm(Demonstrate use of particle swarm optimization in SVM for finding better features and based on cross validation accuracy PSO-SVM in python)
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▍2. PSO_SVR-master
说明： 演示如何在支持向量机中使用粒子群优化，以找到更好的特征并基于交叉验证精度 python中的pso-svm(Demonstrate use of particle swarm optimization in SVM for finding better features and based on cross validation accuracy PSO-SVM in python)
▍3. Deep Learning with Python中英版（含代码）
说明： 深度学习四大名著：Deep Learning with Python 英文版，中文版，Python源码(Four Masterpieces on deep learning : Deep learning with Python English version,Chinese version, python source code)
▍4. LogisticRegression逻辑回归模型
说明： 利用python实现逻辑回归算法预测信用卡违约情况，使用的是海豚大数据大数据分析赛的数据(Using Python to achieve logistic regression algorithm to predict credit card default, using dolphin big data big data analysis game data)
▍5. neural_network神经网络
说明： 利用python实现神经网络算法预测信用卡违约情况，使用的是海豚大数据大数据分析赛的数据(Using Python neural network algorithm to predict credit card default, using dolphin big data big data analysis game data)
▍6. machineLearning
机器学习-分类器-垃圾邮件系统。数据集来自UCI-spambase.data.(Machine Learning- spam classifier.)
▍7. ASVspoof2019_keras
说明： 一个基于kera的resnet神经网络的baseline(A baseline of RESNET neural network based on Kera)
▍8. lidc_nodule_detection
基于LIDC数据集的肺结节检测算法，用于识别肺部ct图像中的肺结节(Pulmonary Nodule Detection Based on LIDC Data Set)
▍9. lidc_nodule_detection
说明： 基于LIDC数据集的肺结节检测算法，用于识别肺部ct图像中的肺结节(Pulmonary Nodule Detection Based on LIDC Data Set)
▍10. TrAdaboost
迁移学习中经典算法Tradaboost的python实现(Python implementation of classical algorithm Tradaboost in transfer learning)
▍11. 基于Tensorflow用CNN（卷积神经网络）处理kdd99数据集ids-kdd99
基于Tensorflow用CNN（卷积神经网络）处理kdd99数据集，代码包括预处理代码和分类代码，准确率99.6%以上，并且快速收敛至最优值。(Based on Tensorflow (convolutional neural network) processing KDD99 data set based on CNN, the code includes preprocessing code and classification code, the accuracy rate is more than 99.6%, and quickly converge to the optimal value.)
▍12. 6.cnn-lstm.py
说明： 对金融时间序列的建模，第一列数据为预测项(For the modeling of financial time series, the first column of data is forecast item.)
▍13. pomdp2json-master
说明： 部分可观察马尔科夫决策过程，强化学习，用于决策(Partially observable Markov Decision process)
▍14. machine learning enample of spam email
说明： 机器学习示例之垃圾邮件识别，包括csv数据和python代码(machine learning example of spam email filter, including csv data and python code)
▍15. timeseries_gan-master
针对非图像数据的GAN代码，数据源的asv文件需要自己制作。(For non image data GAN code, the ASV file of the data source needs to be made by itself.)
▍16. 深度（多层）极限学习机的python实现
说明： 深度极限学习机也叫多层极限学习机，ML-ELM。是黄广斌等人在极限学习机ELM基础上，将其拓展为深度学习的一种模式识别方法，原文文章：Representational learning with extreme learning machine for big data。(The deep extreme learning machine is also called the multi-layer extreme learning machine, ML-ELM. It is Huang Guangbin and others who are based on the extreme learning machine ELM and expand it into a pattern recognition method for deep learning. Original article: Representational learning with extreme learning machine for big data.)
▍17. deep-learning-HAR-master
一份用tensorflow平台做的cnn分类时序信号，是分类UCI 项目中的人体活动识别（HAR）数据集。该数据集包含原始的时序数据和经预处理的数据（包含 561 个特征）(A CNN classification timing signal made by tensorflow platform is a human activity recognition (HAR) dataset in the classified UCI project. The dataset contains original time series data and preprocessed data (including 561 characteristics).)
▍18. gans
一个用于分类MNIT数据集的GAN分类器。(A GAN classifier for categorization of MNIT data sets.)
▍19. chepai
说明： 基于SVM（支持向量机）的车牌识别程序源码，包含训练集与测试集，可识别省市直辖市汉字简称、数字及英文字母。(The source code of license plate recognition program based on SVM (support vector machine), including training set and test set, can recognize the abbreviation of Chinese characters, numbers and English letters of provinces and municipalities directly under the central government.)
▍20. PyTorch深度学习实战
说明： PyTorch深度学习实战从原理到实战、深入浅出地介绍了Facebook人工智能利器Pytorch的卓越表现，只要认真读完本书，你就能掌握Pytorch的使用技巧了。(Pytorch in-depth learning practice introduces the outstanding performance of the Facebook artificial intelligence tool Python from the principle to the practice. As long as you read this book carefully, you can master the use skills of Python.)
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