coursera吴恩达机器学习课程作业自写Python2.7版本,使用jupyter notebook实现,使代码更有层次感,可读性强。
本repository实现算法包括如下:
线性回归: linear_regression.ipynb
多元线性回归:linear_multiple.ipynb
逻辑回归:logic_regression.ipynb
正则化用于逻辑回归: logic_regularization.ipynb
模型诊断+学习曲线: learnCurve.ipynb
一对多分类模型:oneVSall.ipynb
神经网络模型:neuralNetwork.ipynb
SVM分类器:svm.ipynb
kmeans聚类:kmeans.ipynb
pca降维:pca.ipynb
高斯分布用于异常检测:anomaly_detection.ipynb
协调过滤推荐算法:Collaborative_Filter.ipynb
1.课程作业原版是MATLAB版本(填空式编码):对应 machine-learning-ex1——ex8 文件夹
2.kaleko整理的jupyter notebooks版本:对应 coursera_ml_ipynb 文件夹
3.mstampfer对照原版作业格式整理的Python版本,可以尝试自己实现
4.AceCoooool整理的Python版本,有中文注释
5.如果需要了解更多算法知识,本人使用jupyter notebook整理的peter的《机器学习实战》代码
6.本人自写的,关于吴恩达(Andrew Ng)开设的深度学习课程deeplearning.ai的课程答案
ML-code-using-matlab-and-python coursera吴恩达机器学习课程作业自写Python2.7版本,使用jupyter notebook实现,使代码更有层次感,可读性强。 本repository实现算法包括如下: 线性回归: linear_regression.ipynb 多元线性回归:linear_multiple.ipynb 逻辑回归:logic_regressi
0.051267,0.69956,1 -0.092742,0.68494,1 -0.21371,0.69225,1 -0.375,0.50219,1 -0.51325,0.46564,1 -0.52477,0.2098,1 -0.39804,0.034357,1 -0.30588,-0.19225,1 0.016705,-0.40424,1 0.13191,-0.51389,1 0.38537,-
ML-code-using-matlab-and-python coursera吴恩达机器学习课程作业自写Python2.7版本,使用jupyter notebook实现,使代码更有层次感,可读性强。 本repository实现算法包括如下: 线性回归: linear_regression.ipynb 多元线性回归:linear_multiple.ipynb 逻辑回归:logic_regressi
ML-code-using-matlab-and-python coursera吴恩达机器学习课程作业自写Python2.7版本,使用jupyter notebook实现,使代码更有层次感,可读性强。 本repository实现算法包括如下: 线性回归: linear_regression.ipynb 多元线性回归:linear_multiple.ipynb 逻辑回归:logic_regressi
package wikipedia import org.apache.spark.SparkConf import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ import org.apache.spark.rdd.RDD case class WikipediaArticle(title: St
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