文件名称:machineLearning-master
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2015-03-16
- 文件大小：
- 15.29mb
- 下载次数：
- 0次
- 提 供 者：
- l*
- 相关连接：
- 无
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- 别用迅雷下载，失败请重下，重下不扣分！
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python编写的一些机器学习算法,包含监督学习和非监督学习-Some machine learning algorithms written in python, including supervised learning and unsupervised learning
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下载文件列表
machineLearning-master
......................\LICENSE.txt
......................\README.md
......................\diagnosticTests
......................\...............\README.md
......................\...............\ex5.m
......................\...............\ex5data1.mat
......................\...............\featureNormalize.m
......................\...............\fmincg.m
......................\...............\learningCurve.m
......................\...............\linearRegCostFunction.m
......................\...............\plotFit.m
......................\...............\polyFeatures.m
......................\...............\submit.m
......................\...............\submitWeb.m
......................\...............\trainLinearReg.m
......................\...............\validationCurve.m
......................\imagesForExplanation
......................\....................\ArtificialNeuronModel.jpg
......................\....................\ArtificialNeuronSimulateLogicalAND.jpg
......................\....................\CostFunctionExampleWithTheta_0AndTheta_1.jpg
......................\....................\GradientDescentWithMutlipleLocalMinimum.jpg
......................\....................\LabeledNeuron.jpg
......................\....................\NeuralNetwork.jpg
......................\....................\NeuralNetworkEquations.jpg
......................\....................\UnderFitAndOverFit.jpg
......................\....................\equations
......................\....................\.........\gradientDescentUpdateTheta_j.gif
......................\supervisedLearning
......................\..................\LinearAlgebraReview.md
......................\..................\linearRegressionIn1Variable
......................\..................\...........................\README.md
......................\..................\...........................\computeCost.m
......................\..................\...........................\gradientDescent.m
......................\..................\...........................\inputTrainingSet.txt
......................\..................\...........................\plotData.m
......................\..................\...........................\run.m
......................\..................\linearRegressionInMultipleVariables
......................\..................\...................................\README.md
......................\..................\...................................\computeCostMulti.m
......................\..................\...................................\featureNormalize.m
......................\..................\...................................\gradientDescentMulti.m
......................\..................\...................................\inputTrainingSet.txt
......................\..................\...................................\normalEquation.m
......................\..................\...................................\run.m
......................\..................\logisticRegression
......................\..................\..................\README.md
......................\..................\..................\costFunction.m
......................\..................\..................\costFunctionExample.m
......................\..................\..................\costFunctionReg.m
......................\..................\..................\inputTrainingSet1.txt
......................\..................\..................\inputTrainingSet2.txt
......................\..................\..................\mapFeature.m
......................\..................\..................\plotData.m
......................\..................\..................\plotDecisionBoundary.m
......................\..................\..................\predict.m
......................\..................\..................\runExample.m
......................\..................\..................\runRegularizedExample.m
......................\..................\..................\sigmoid.m
.................