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NTU-Coursera ml: HomeWork 1 Q15-20Question15 The training data format is as follows: The input has four dimensions, and the output is {-1, + 1 }. There are a total of 400 data records. The question requires that the weight vector element be initialized to 0, and then "Naive Cycle" is used to traverse the training set. When the iteration is stopped, the weight vector is updated several times. The so-called "Naive Cycle" means that after an error i

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-Machine learning-all programming practice answers In this exercise, you will implement the K-means clustering algorithm and apply it to compressed images. In the second section, y

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 threadImplementation of threading mechanismUser-level threads, c

full implementation of multi-layered neural network recognition picture of the cat Original Coursera Course homepage, in the NetEase cloud classroom also has the curriculum resources but no programming practice. This program uses the functions completed in the last job, fully implementing a multilayer neural network, and training to identify whether there is a cat in the picture. There is no comment in the Code and Training test data download Cod

(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