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For example, consider the computation % % sigmoid(X * theta) % % Each row of the resulting matrix will contain the value of the % prediction for that example. each parameter in theta % % Hint: The computation of the cost function and gradients can be % efficiently vectorized. % Compute the partial derivatives and set grad to the partial % derivatives of the cost w.r.t.
VIDEO DIARY WEEK 4 CODE
% Initialize some useful values m = length ( y ) % number of training examples % You need to return the following variables correctly J = 0 grad = zeros ( size ( theta )) % = YOUR CODE HERE = % Instructions: Compute the cost of a particular choice of theta. * indicates files you will need to completeįunction = lrCostFunction (theta, X, y, lambda ) %LRCOSTFUNCTION Compute cost and gradient for logistic regression with %regularization % J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using % theta as the parameter for regularized logistic regression and the % gradient of the cost w.r.t.
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Video - YouTube videos featuring Free IOT/ML tutorials.predict.m - Neural network prediction function.predictOneVsAll.m - Predict using a one-vs-all multi-class classifier.oneVsAll.m - Train a one-vs-all multi-class classifier.lrCostFunction.m - Logistic regression cost function.fmincg.m - Function minimization routine (similar to fminunc).displayData.m - Function to help visualize the dataset.submit.m - Submission script that sends your solutions to our servers.ex3weights.mat - Initial weights for the neural network exercise.ex3data1.mat - Training set of hand-written digits.ex3 nn.m - Octave/MATLAB script that steps you through part 2.ex3.m - Octave/MATLAB script that steps you through part 1.Fast.ai: Introduction to Machine Learning for Coders.Coursera: Advanced Machine Learning Specialization.