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Support Vector MachinesI have the some issues to state. First, there were some bugs in original code which is caused by versions. I don ' t know ...There is three pictures u need to draw a division boundary. The first calls ' VISUALIZEBOUNDARYLINEAR.M ' which is fine and the others which call ' visualizeboundary.m ' can notDraw boundaries. So I check out this file and change the code ' contour (X1, X2, Vals, [0 0], ' Color ', ' B '); ' to ' Contour (X1, X2, Vals, [0.1 0.1], ' LineColor ', ' B ')

This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural network, the back is not necessarily seen.Of course, look at the process is still to do the notes to do homework, or read it is also a curs

Multivariate regressionReview simple linear regression: A feature, two correlation coefficients　　The actual application is much more complicated than this, such as1, house prices and housing area is not just a simple linear relationship.2, there are many factors affecting the price, not only the size of the house, but also many other factors.　　　　Now, in the first case, the price and the housing area are not simply linear, and may be two or polynomial:Two times function:　　Polynomial functions:　　P

? This is determined by the characteristic value of the feature. There are two kinds of discrete value and continuous value, the distribution of discrete values is Poisson distribution, Bernoulli distribution, the distribution of continuous values is uniform distribution, normal distribution, chi-square distribution and so on. The reason why we assume the two eigenvalues of the above example is normal distribution is because the distribution of the majority of continuous-value variables

Questions -31 point Possible (graded) Total time limit: 1000ms Memory Limit: 65536kB Describe Write a two-dimensional array class Array2, so that the following program output is: 0,1,2,3, 4,5,6,7, 8,9,10,11, Next 0,1,2,3, 4,5,6,7, 8,9,10,11, Program: #include Add your code here int main () { Array2 a (3,4); int i,j; fo

)/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) $

Overview Cost Function and BackPropagation Cost Function BackPropagation algorithm BackPropagation Intuition Back propagation in practice Implementation Note:unrolling Parameters Gradient Check Random initialization Put It together Application of Neural Networks Autonomous Driving Review Log 2/10/2017:all the videos; Puzzled about Backprogation 2/11/2017:reviewed backpropaga

Someting about Lists mutation1 ###################################2 #Mutation vs. Assignment3 4 5 ################6 #Look alike, but different7 8A = [4, 5, 6]9b = [4, 5, 6]Ten Print "Original A and B:", A, b One Print "is they same thing?"+ F isb A -A[1] = 20 - Print "New A and B:", A, b the Print - - ################ - #aliased + -c = [4, 5, 6] +D =C A Print "Original C and D:", C, D at Print "is they same thing?"+ D isD - -C[1] = 20 - Print "New C and D:", C, D - Print - in ##############

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

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 gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Text detection Character segmentation Character classification Using s

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

(Paddle2_pos,1,'Blue',' White') #determine whether paddle and ball collide ifBall_pos[0] Pad_width:ifBALL_POS[1] >= paddle1_pos[0][1] andBALL_POS[1] ]: Spawn_ball (right)Else: Score2+ = 1ifBall_pos[0] >= Width-pad_width-Ball_radius:ifBALL_POS[1] >= paddle2_pos[0][1] andBALL_POS[1] ]: Spawn_ball (left)Else: Score1+ = 1#Draw scoresCanvas.draw_text (str (score1), [WIDTH/2-40, 40], 30,' White') Canvas.draw_text (str (score2), [WIDTH/2 + 20, 40], 30,' White')defKeyDown (key):GlobalPaddle1_vel,

(stop_num)#define event handlers for buttons; "Start", "Stop", "Reset"defStart_handler (): Timer.start ()defStop_handler (): Timer.stop ()defReset_handler (): Timer.stop ()GlobalTGlobalt_str Reset_score ()#Define event handler for timer with 0.1 sec intervaldefTimer_handler ():GlobalTGlobalT_str T= t + 1T_str=format (t)defTimer_score_handler (): Update_score ()#Define Draw HandlerdefDraw_handler (Canvas): Canvas.draw_text (t_str, Position,36," White") Canvas.draw_text (SCORE_STR, [160, 20], 16,

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

y += 1 timer.stop() elif timer.is_running(): y += 1 timer.stop() def reset(): global t, x, y t = 0 x = 0 y = 0 timer.stop()# define event handler for 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)

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.

m>=10n and uses multiple Gaussian distributions.In practical applications, the original model is more commonly used, the average person will manually add additional variables.If the σ matrix is found to be irreversible in practical applications, there are 2 possible reasons for this:1. The condition of M greater than N is not satisfied.2. There are redundant variables (at least 2 variables are exactly the same, XI=XJ,XK=XI+XJ). is actually caused by the linear correlation of the characteristic

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 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508The graphic is represented as follows:As

, the weight of the high-weighted data is increased by 1000 times times the probability, which is equivalent to replication. However, if you are traversing the entire test set (not sampling) to calculate the error, there is no need to modify the call probability, just add the weights of the corresponding errors and divide by N. So far, we have expanded the VC Bound, which is also set up on the issue of multiple classifications!SummaryFor more discussion and exchange on machine learning, please