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This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA Download coursera-Wunda

)/m; at End - End - -%size (J,1) -%size (J,2) - ind3 = A3-Ty; -D2 = (D3 * THETA2 (:,2: End)). *sigmoidgradient (z2); toTheta1_grad = Theta1_grad + d2'*a1/m; +Theta2_grad = Theta2_grad + d3'*a2/m; - the% ------------------------------------------------------------- *jj=0; $ Panax Notoginseng forI=1: Size (Theta1,1) - forj=2: Size (Theta1,2) theJJ = JJ + Theta1 (i,j) *theta1 (i,j) *lambda/(m*2); + End A End theSize (Theta1,1); +Size (Theta1,2); - $ forI=1: Size (THETA2,1) $

IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford Andrew Ms Ng's tutorials in Coursera and online courses such as UFLDL Tuto

First, how to learn a large-scale data set?In the case of a large training sample set, we can take a small sample to learn the model, such as m=1000, and then draw the corresponding learning curve. If the model is found to be of high deviation according to the learning curve, the model should continue to be adjusted on

Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no unanimous answer to the definition of machine learning.Arthur Samuel (1959) gives a definition of machine learning:Machine

9 Anomaly Detection9.1 Density Estimation9.1.1 Problem MotivationAnomaly detection (Density estimation) is a common application of machine learning and is mainly used for unsupervised learning, but in some ways it is similar to supervised learning.The most common application of anomaly detection is fraud detection and in the industrial production field.In particular, the industrial production of aircraft en

points of mini project are translated, Then translate the Mini project implementation steps, not a one-time full translation, take too long, the previous translation may forget, and the translation may not be accurate, and sometimes to see the original text. Complete a paragraph and translate the next paragraph, step by step. Do not translate all, some do not help to complete the task can not translate, save time. 4. Selective translation of code clinic,5. If you get stuck, search for keywords

Https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial Octave Tutorial 5 questions 1.Suppose I first execute the following Octave commands: A = [1 2; 3 4; 5 6]; B = [1 2 3; 4 5 6]; Which of the following is then valid Octave commands? Check all, apply and assume all options is written in an Octave command. (HINT:A ' denotes the transpose of A.)

use of MATLAB. *.4.gradientdescent.mfunction [Theta, j_history] =gradientdescent (X, y, theta, Alpha, num_iters)%gradientdescent performs gradient descent to learn theta% theta = gradientdescent (X, y, theta, Alpha, num_iters) up Dates theta by% taking num_iters gradient steps with learning rate alpha% Initialize Some useful valuesm= Length (y);%Number of training examplesj_history= Zeros (Num_iters,1); f

minimum functionRegular equation method gradient descent can be better extended to large datasets for a large number of contexts and machine learning next-important extensions The regular equation of extended numerical solution of two algorithms in order to solve the minimization problem of [min J (θ0,θ1)], we use the exact numerical method rather than the constant iterative gradient descent method with the advantages and disadvantages of the regular

Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introducedThe composition of the threadImplementatio