我是靠谱客的博主 火星上唇膏，这篇文章主要介绍Machine Learning- Coursera - Stanford - Programming ExercisesIV. Linear Regression with Multiple Variables (Week 2) Programming Exercise 1VII. Regularization (Week 3) Programming Exercise 2VIII. Neural Networks: Representation (Week 4) Programming ，现在分享给大家，希望可以做个参考。
IV. Linear Regression with Multiple Variables (Week 2) Programming Exercise 1
1) Warm up exercise [ warmUpExercise.m ]
2) Computing Cost (for one variable) [ computeCost.m ]
3) Gradient Descent (for one variable) [ gradientDescent.m ]
4) Feature Normalization [ featureNormalize.m ]
5) Computing Cost (for multiple variables) [ computeCostMulti.m ]
6) Gradient Descent (for multiple variables) [ gradientDescentMulti.m ]
7) Normal Equations [ normalEqn.m ]
VII. Regularization (Week 3) Programming Exercise 2
1) Sigmoid Function [ sigmoid.m ]
Octave expression:
2) Logistic Regression Cost [ costFunction.m ]
Vectorization Implementation：
3) Logistic Regression Gradient [ costFunction.m ]
4) Predict [ predict.m ]
5) Regularized Logistic Regression Cost [ costFunctionReg.m ]
not including theta(0) , same as theta(1) in Octave
6) Regularized Logistic Regression Gradient [ costFunctionReg.m ]
reset the value of grad(1) in line two
5 and 6 can also represented as follows:
VIII. Neural Networks: Representation (Week 4) Programming Exercise 3
1) Vectorized Logistic Regression [ lrCostFunction.m ]
2) One-vs-all classifier training [ oneVsAll.m ]
3) One-vs-all classifier prediction [ predictOneVsAll.m ]
4) Neural network prediction function [ predict.m ]
IX. Neural Networks: Learning (Week 5) Programming Exercise 4
1) Feedforward and Cost Function [ nnCostFunction.m ]
hTheta(x) is the vector with K-dimension
2) Regularized Cost Function [ nnCostFunction.m ]
3) Sigmoid Gradient [ sigmoidGradient.m ]
4) Neural Network Gradient (Backpropagation) [ nnCostFunction.m ]
cost lots of time.....
5) Regularized Gradient [ nnCostFunction.m ]
X. Advice for Applying Machine Learning (Week 6) Programming Exercise 5
1) Regularized Linear Regression Cost Function [ linearRegCostFunction.m ]
2) Regularized Linear Regression Gradient [ linearRegCostFunction.m ]
3) Learning Curve [ learningCurve.m ]
4) Polynomial Feature Mapping [ polyFeatures.m ]
5) Validation Curve [ validationCurve.m ]
not including regularization
XII. Support Vector Machines (Week 7) Programming Exercise 6
1) Gaussian Kernel [ gaussianKernel.m ]
2) Parameters (C, sigma) for Dataset 3 [ dataset3Params.m ]
calcate the parameters based on training set for every C and sigma pair. Then calcate the cross validation error based on cross validation set for every pair. Choose the minimum pair for cross validation error.
3) Email Preprocessing [ processEmail.m ]
4) Email Feature Extraction [ emailFeatures.m ]
XIV. Dimensionality Reduction (Week 8) Programming Exercise 7
1) Find Closest Centroids (k-Means) [ findClosestCentroids.m ]
2) Compute Centroid Means (k-Means) [ computeCentroids.m ]
vectorization implementation
3) PCA [ pca.m ]
4) Project Data (PCA) [ projectData.m ]
vectorization implementation
5) Recover Data (PCA) [ recoverData.m ]
XVI. Recommender Systems (Week 9) Programming Exercise 8
1) Estimate Gaussian Parameters [ estimateGaussian.m ]
2) Select Threshold [ selectThreshold.m ]
3) Collaborative Filtering Cost [ cofiCostFunc.m ]
4) Collaborative Filtering Gradient [ cofiCostFunc.m ]
5) Regularized Cost [ cofiCostFunc.m ]
6) Regularized Gradient [ cofiCostFunc.m ]
最后
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