estimateGaussian.m mu = 1/m * sum(X); %求解平均值 sigma2 = 1/m * sum((X - repmat(mu, m, 1)).^2); %求解方差的平均值 selectThreshold.m %进行预测 predictions = (pval < epsilon); %计算混淆矩阵 fp = sum((predi

pca.m sigma = (X'*X)./m; %计算Sigma [U,S,V] = svd(sigma); %进行奇异值分解 projectData.m Ureduce = U(:, 1:K); %提取前k列 for i = 1:size(X,1) %遍历所有样本 x = X(i,:); %获取样本 Z(i,:) = x*Ureduce; %进行降维 end r

lrCostFunction.m (logistic regression cost function) J = 1./m*(-y'*log(sigmoid(X*theta))-(1-y')*log(1-sigmoid(X*theta))); %计算代价函数J J = J + lambda/(2*m)*(sum(theta.^2)-theta(1).^2); %引入正则项，注意去掉常数项系数

sigmoid.m g = 1./(1+exp(-z)); %对每个元素都进行操作 costFunction.m J = 1./m*(-y'*log(sigmoid(X*theta)) - (1-y)'*log(1-sigmoid(X*theta))); grad = 1/m * X'*(sigmoid(X*theta) - y); predict.m p = round(sigmoid(X

Some Points:   The Results: linearRegCostFunction.m function [J, grad] = linearRegCostFunction(X, y, theta, lambda) % Initialize some useful values m = length(y); % number of training examples % You

warmUpExercise.m A = eye(5); plotData.m plot(x, y, 'rx', 'MarkerSize', 10); ylabel('Profit in $10,000s'); xlabel('Population of City in 10,000s'); gradientDescent.m theta_temp = theta; %记录当前梯度

sigmoidGradient.m g = sigmoid(z) .* (1 - sigmoid(z)); %y*(1-y) randInitializeWeights.m epsilon_init = 0.11; %初始化值 W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init; %完成随机初始化 nnCostFunct

My Solution to Assignments of Machine-Learning on Coursera This is my solution to the assignments of Machine-Learning on Coursera.Machine-Learning on Coursera isa classical class, whose advisor is And

Deep Learning Specialization Projects from the Deep Learning Specialization from deeplearning.ai offered by Coursera. Instructor: Andrew Ng Master Deep Learning and Break Into AI If you want to break

Deep Learning Specialization on Coursera Instructor: Andrew Ng Just completed Deep Learning Specialization on Coursera, using this repo to keep a record of all my completed coursework and related file

斯坦福大学2014（吴恩达）机器学习教程中文笔记 课程地址：https://www.coursera.org/course/ml 笔记在线阅读 Machine Learning(机器学习)是研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。它是人工智能的核心，是使计算机具有智能的根本途径，其应用遍及人工智能的各个领域，它主要使用归纳、综合而

cd json-serverjson-server --watch db.json -d 2000tns run android --emulator

Coursera-Machine-Learning-Andrew-NG This is a repository of my coursera Machine Learning by Standford, Andrew NG course's assignments' solution This is all written in matlab.The datasets are also uplo