▍1. 神经网络,全连接层
神经网络,全连接层
说明: 随机森林数据预测,判断数据重要性,给出预测(Random forest data prediction)
回声状态网络ESN源代码,python版,适用与python3和python2,包括输入层到输出层连接(Echo state networks python code, suit for python3 and python2, including input units to output units links)
说明: 利用python编写室内定位机器学习,计算结果,室内定位(Indoor Localization by wifi)
利用python编写室内定位机器学习,计算结果,室内定位(Indoor Localization by wifi)
根据SVM,基于向量机算法的入侵检测系统,依托的是KDD99数据集(According to SVM, the intrusion detection system based on vector machine algorithm relies on KD99 data set.)
说明: 根据SVM,基于向量机算法的入侵检测系统,依托的是KDD99数据集(According to SVM, the intrusion detection system based on vector machine algorithm relies on KD99 data set.)
说明: k-means聚类算法,spectral clustering,手写数字识别,数据分类(K-means clustering algorithm, spectral clustering, handwritten number recognition, data classification)
基于knn算法的入侵检测模型,利用python代码实现,包含了源代码,测试集,和训练集。(This is a python file)
说明: 基于knn算法的入侵检测模型,利用python代码实现,包含了源代码,测试集,和训练集。(This is a python file)
一种BP神经网络的PYTHON代码,可用于简单预测等(A BP neural network PYTHON code)
Pytho实现语言bp神经网络,内含数据集,适合初学者。(Pytho implementation language bp neural network, containing data sets for beginners.)
python语言实现k-means算法和Fast Search And Find Of Density Peaks算法用于文本聚类,(python language implements k-means algorithm and Fast Search And Find Of Density Peaks for text clustering algorithm,)
人工蜂群算法 算法 用户均衡模型 frankwolfe算法(Artificial Bee Colony Algorithms User Equilibrium Model Frankwolfe Algorithms)
说明: 人工蜂群算法 算法 用户均衡模型 frankwolfe算法(Artificial Bee Colony Algorithms User Equilibrium Model Frankwolfe Algorithms)
运用卷积神经网络来提取图片的特征值并用SVM做出分类(using CNN And SVM to train my pictures.)
说明: 渐进的让生成器和判别器增长:从一个低分辨率开始,随着训练发展,不断添加新层使模型增加更好的细节。(Let the generator and discriminator grow gradually: start with a low resolution and add new layers to the model with the development of training.)
使用SVM算法对CIFAR-10图片数据集进行分类,包括模型的训练,测试和参数的调优(Using SVM algorithm to classify CIFAR-10 image data sets, including model training, testing and parameter tuning)
说明: 新闻系统分类,能够对新闻进行十个种类的分类,财经,体育等模块(News system classification)!注意,文件有密码,不知道密码