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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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