coursera

Week 4 Practice quizhelp Center The Warning:the hard deadline has passed. You can attempt it, Butyou won't get credit for it. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify This answers here are I own work. Question 1 Can a crawler that only follows hyperlinks identify hidden pages, does not have any incoming links? No Yes question 2 after obtaining the chunk's handle and locations from th

continuous stream of users to a website, we can run an endless loop that gets (x, y), where we collect some user act Ions for the features in X to predict some behavior Y. You can updateθfor each individual (x, y) pair as you collect them. This is the can adapt to new pools of users, since is continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gr

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 said to learn from experience E with R Espect to some class of tasks T and performance measure P, if it performance at tasks in T, as measured By P, improves with experience E."Examp

different behavior. Suppose you has an 7x7 matrix x. You want to compute the log of every element, the square of every element, add 1 to every element, and divide every elemen T by 4. You'll store the results in four matrices, A,B,C,D. One-by-doing is the following code: For i = 1:7 for j = 1:7 A (i, j) = log (X (i, j)); B (i, j) = X (i, j) ^ 2; C (i, j) = X (i, J) + 1; D (i, j) = X (i, j)/4; End End Which of the following correctly compute A,B,C, or D? Check

fluctuates around true error, close to the value of true error.　　As can be seen, summed up the judgment of fit:Estimate parameter W' in the model1. Training error (W) 2. True Error (W) > True error (w ')Description W over fitting3 Sources of errorNoise, Bias, Variance1. Noise NoiseOf an inherent, irreducible, or reduced nature.　　　2, Bias Deviation　　　　　　The simpler the model, the greater the deviation　　The more complex the model, the smaller the deviation3. Variance Variance　　　　Simple model, sma

-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T

Week 4 Quizhelp Center Warning:the hard deadline has passed. You can attempt it, Butyou won't get credit for it. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify This answers here are I own work. Question 1 Which of the following is nottrue about GFS? The GFS keeps multiple replicas of the same file chunk. The file data transfer happens directly between the GFS client and the GFS chunkservers

Week 2 Quizhelp Center Warning:the hard deadline has passed. You can attempt it, but and you won't be. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify this answers here are I own work. Question 1 Suppose a query has a total of 4 relevant documents in the collection. System A and System B have each retrieved, and the relevance status of the ranked lists is shown below: System A: [-----------]

[' Down']: Paddle2_vel= 8defKeyUp (key):GlobalPaddle1_vel, Paddle2_velifKey = = Simplegui. key_map['W']: Paddle1_vel=0elifKey = = Simplegui. key_map['s']: Paddle1_vel=0ifKey = = Simplegui. key_map[' up']: Paddle2_vel=0elifKey = = Simplegui. key_map[' Down']: Paddle2_vel=0defButton_restart (): New_game ()#Create frameframe = Simplegui.create_frame ("Pong", WIDTH, HEIGHT) Frame.set_draw_handler (Draw) Frame.set_keydown_handler (KeyDown) Frame.set_keyup_handler (KeyUp) Frame.add_button ("Restart",

handlersFrame.add_button ("Start", Start_handler) Frame.add_button ("Stop", Stop_handler) Frame.add_button ("Reset", Reset_handler) Frame.set_draw_handler (draw_handler) Timer=Simplegui.create_timer (interval, timer_handler) Timer_score=Simplegui.create_timer (interval, timer_score_handler)#Start FrameFrame.start () Timer_score.start ( )#Remember to review the grading rubrichttp://www.codeskulptor.org/#user40_VuvumcScXK0k0FO. py Coursera-an Intro

We recommend the responsive programming course on Coursera, an advanced Scala language course. At the beginning of the course, we proposed an Application Scenario: constructing a JSON string. If you do not know the JSON string, you can simply Google it. To do this, we define the following classes abstract class JSON case class JSeq(elems: List[JSON]) extends JSON case class JObj(bindings: Map[String, JSON]) extends JSON case class JNum(num: Double) e

#include using namespacestd;/*int Wanmeifugai (int n) {if (n%2) {return 0; } else if (n==2) {return 3; }else if (n = = 0) return 1; else return (3*3) *wanmeifugai (n-4);}*///The following is a reference to the online program/*Ideas: Citation:http://m.blog.csdn.net/blog/njukingway/20451825First: F (n) = 3*f (n-2) + ... f (n) = 3*f (n-2) + 2*f (n-4) +....//just now our recursion is pushed in the smallest unit (3 blocks), but there are large units of small units (6, 9, 12 blocks, etc.) There

timer with 0.1 sec intervaldef tick(): global t t += 1#不需要return# define draw handlerdef draw(canvas): canvas.draw_text(format(t), [80, 120], 50, "White") canvas.draw_text(str(x) + "/" + str(y), [220, 30], 35, "Green")# create framef = simplegui.create_frame("Stopwatch", 300, 200)# register event handlersf.add_button("Start", start, 100)f.add_button("Stop", stop, 100)f.add_button("Reset", reset, 100)f.set_draw_handler(draw)timer = simplegui.create_timer(100, tick)# start framef.star

Week 2 gradient descent for multiple variables [1] multi-variable linear model cost function Answer: AB [2] feature scaling feature Scaling Answer: d 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: [Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford machine learning.

covariance matrix is a diagonal matrix, the detection formula of the anomaly detection algorithm using the multivariate Gaussian distribution and the non-multivariate Gaussian distribution is the same.2.1 The original model manually creates new feature variables to calculate outliers, and the new model automatically calculates the correlations between the different features.2.2 The original model calculation is small, it is suitable for large-scale characteristic variable (n Large), and the new

parameters Theta%compute JCV and Jval without Regularization,causse last arguments (lambda) is zero error_train (i) = Linearregcostfunction (X, y, th ETA, 0);% Calculation Training error error_val (i) = Linearregcostfunction (Xval, Yval, theta, 0);% compute cross-validation error endFor these 10 different lambda, the calculated training error and cross-validation error are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597

will be training data in all marked-1 points copied 1000 times, if the approximate function at these points error, there will be 1000 times times the penalty. This problem is turned into a weight-free problem:And we already know that the pokect algorithm can solve the problem of no weight.In fact, in the application we will not really copy some data 1000 times, we only need to calculate the error, the weight of the high-weighted data is increased by 1000 times times the probability, which is eq

that meets a specific condition (assertion )? 2. The specific condition (assertion) is mapped to f, which is equal to the input parameter. Remember that the return type is Set, that is, Int => Boolean type function (first-class citizen !)! Conclusion In general, Scala is different from other object-oriented languages. One of the biggest features is that it integrates a lot of functional programming concepts and methods. This is especially important when using Scala! Scala can be seen as a comb

to find someθ0,θ1 such that J (θ0,θ1) =0. Which of the statements below must then be true? (Check all, apply.)Answer For the is true, we must haveθ0=0 andθ1=0 so, hθ (x) =0 If J (θ0,θ1) =0, that means the line defined by the equation "y=θ0+θ1x" perfectly fits all of our data. There's no particular reason to expect so the values ofθ0 andθ1 that achieve this is both 0 (unless y (i) =0 for all of Our training examples). Our training set can is fit perfectly by a straight lin

-Learning RateIn the gradient descent algorithm, the number of iterations required for the algorithm convergence varies according to the model. Since we cannot predict in advance, we can plot the corresponding graphs of iteration times and cost functions to observe when the algorithm tends to converge.Of course, there are some ways to automatically detect convergence, for example, we compare the change value of a cost function with a predetermined threshold, such as 0.001, to determine convergen