CheeseZH: Stanford University: Machine Learning Ex1:Linear Regression

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CheeseZH: Stanford University: Machine Learning Ex1:Linear Regression

(1) How to comput the Cost function in Univirate/Multivariate Linear Regression; (2) How to comput the Batch Gradient Descent function in Univirate/Multivariate Linear Regression; (3) How to scale features by mean value and standard deviation; (4) Ho…

CheeseZH: Stanford University: Machine Learning Ex2:Logistic Regression

1. Sigmoid Function In Logisttic Regression, the hypothesis is defined as: where function g is the sigmoid function. The sigmoid function is defined as: 2.Cost function and gradient The cost function in logistic regression is: the gradient of the cos…

CheeseZH: Stanford University: Machine Learning Ex5:Regularized Linear Regression and Bias v.s. Variance

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CheeseZH: Stanford University: Machine Learning Ex3: Multiclass Logistic Regression and Neural Network Prediction

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CheeseZH: Stanford University: Machine Learning Ex4:Training Neural Network(Backpropagation Algorithm)

1. Feedforward and cost function; 2.Regularized cost function: 3.Sigmoid gradient The gradient for the sigmoid function can be computed as: where: 4.Random initialization randInitializeWeights.m function W = randInitializeWeights(L_in, L_out) %RANDIN…

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【Coursera - machine learning】 Linear regression with one variable-quiz

Question 1 Consider the problem of predicting how well a student does in her second year of college/university, given how well they did in their first year. Specifically, let x be equal to the number of "A" grades (including A-. A and A+ grades)…

machine learning (2)-linear regression with one variable

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ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS

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Stanford CS229 Machine Learning by Andrew Ng

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