coursera refund

System. Security. Cryptography. CryptographicException occurs when the refund interface is called. Internal error solution is displayed. ios3194 error Solution I have summarized three reasons why the certificate cannot be loaded: 1. The certificate password is incorrect. The certificate password is the merchant ID. Solution: Check whether the certificate password is consistent with the merchant ID. 2. the IIS settings are incorrect. The user confi

normal accesspublic static String Return_url = "http://merchant url/create_direct_pay_by_user-java-utf-8/return_url.jsp";Signature methodpublic static String Sign_type = "RSA";For debugging, create a TXT log folder path, see the Logresult (String Sword) printing method in the Alipaycore.java class.public static String Log_path = "c:\\";The character encoding format currently supports GBK or Utf-8public static String Input_charset = "Utf-8";Payment type, no modification requiredpublic static Str

Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully consider each English word, or easy to make mistakes;(3) I am using MATLAB to submit the programming job, because of the MATLAB command is

Took a course on software security at Coursera. Here is a list of readings from the professor:Week 1ReadingsRequired ReadingThe only required reading this week is the following: Common Vulnerabilities Guide for C programmers. Take note of the unsafe C library functions listed here, and how they is the source of the buffer overflow vulnerabilities. This list is relevant for the project and this week ' s quiz. (Reference) Memory layout. Exp

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 Tutorial,stanford cs231n and Tutorial, as well as a large number of online related materials (listed later).　PrefaceThis article mainly int

Week 1 Practice 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 Consider the instantiation of the vector space model where documents and queries are represented as term Ency vectors. Assume we have the following query and two documents: Q = "Future of on

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 vectorizations correctly compute Z? Check all the Apply.

(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, small variance　　Complex model, large variance　　Deviations and variance tradeoffs, deviations and variances cannot be calculated　　　　Training error and the amount of test data, fixed model complexity, a

-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: [-----------]

(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