coursera machine learning week 1 quiz answers 2018

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Coursera Machine Learning second week quiz answer Octave/matlab Tutorial

would the Vectorize this code to run without all for loops? Check all the Apply. A: v = A * x; B: v = Ax; C: V =x ' * A; D: v = SUM (A * x); Answer: A. v = a * x; v = ax:undefined function or variable ' Ax '. 4.Say you has a vectors v and Wwith 7 elements (i.e., they has dimensions 7x1). Consider the following code: z = 0; For i = 1:7 Z = z + V (i) * W (i) End Which of the following vectorizati

"Coursera-machine learning" Linear regression with one Variable-quiz

, i.e., all of our training examples lie perfectly on some straigh T line. If J (θ0,θ1) =0, that means the line defined by the equation "y=θ0+θ1x" perfectly fits all of our data. For the To is true, we must has Y (i) =0 for every value of i=1,2,..., m. So long as any of our training examples lie on a straight line, we'll be able to findθ0 andθ1 so, J (θ0,θ1) =0. It is not a necessary that Y (i) =0 for all of our examples. We can perfectly predict the value o

Neural Network jobs: NN Learning Coursera machine learning (Andrew Ng) WEEK 5

)/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);

Note for Coursera "Machine learning" 1 (1) | What are machine learning?

What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was sa

coursera-Wunda-Machine learning-(programming exercise 7) K mean and PCA (corresponds to the 8th week course)

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: Exercise 7--k-means and PCA Download

Coursera Machine Learning second week programming job Linear Regression

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

Coursera-machine Learning, Stanford:week 1

Welcome and Introductionoverviewreadinglog 9/9 videos and quiz completed; 10/29 Review; Note1.1 Welcome 1) What are machine learning? Machine learning are the science of getting compters to l

Stanford Machine Learning Week 1-single variable linear regression

found by gradient descent: '); fprintf ('%f%f \ n ', theta (1), Theta (2));% Plot the Lin Ear fithold on; % Keep previous plot visibleplot (X (:, 2), X*theta, '-') Legend (' Training data ', ' Linear regression ') hold off% don ' t overlay Any more plots on the figure% Predict values for population sizes of 35,000 and 70,000predict1 = [1, 3.5] *theta;fprintf (' for population = 35,000, we predict a profit

Machine LEARNING-III. Linear Algebra Review (Week 1, Optional)

algebra review, I'll be the using one index vectors. Most vector subscripts in the course start from 1.When talking on machine learning applications, sometimes explicitly say if we need to switch to, when we need to use The zero index vectors as well. Discussion of machine learnin

Machine learning Week 1

(data).Note that we had separated out of the cases forθJ into separate equations forθ0 andθ1 ; and that forθ1 We are multiplyingxi At the end of due to the derivative. The following is a derivation of??θJJ(θ) For a single example:The point of any this is so if we start with a guess for our hypothesis and then repeatedly apply these gradient descent Equations, our hypothesis would become and more accurate.So, this is simply gradient descent in the original cost function J. This method loo

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