machine_learning's Introduction

Machine_Learning

Machine Learning by Andrew Ng on Coursera

Ex1 Linear Regression

$J(\theta)$
Gradient descent
$J(\theta)$ with multiple variables
- Study about learning rate
Feature Normalization
Normal Equations

Ex2 Logistic Regression

plotData.m - Function to plot 2D classification data
sigmoid.m - Sigmoid Function
costFunction.m - Logistic Regression Cost Function
predict.m - Logistic Regression Prediction Function
costFunctionReg.m - Regularized Logistic Regression Cost

Ex3 Multi-class Classification and Neural Networks

lrCostFunction.m - Logistic regression cost function
oneVsAll.m - Train a one-vs-all multi-class classifier
predictOneVsAll.m - Predict using a one-vs-all multi-class classifier
predict.m - Neural network prediction function

Ex4 Neural Networks Learning

sigmoidGradient.m - Compute the gradient of the sigmoid function
randInitializeWeights.m - Randomly initialize weights
nnCostFunction.m - Neural network cost function

Ex5 Regularized Linear Regression and Bias v.s. Variance

linearRegCostFunction.m - Regularized linear regression cost function
learningCurve.m - Generates a learning curve
polyFeatures.m - Maps data into polynomial feature space
validationCurve.m - Generates a cross validation curve

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