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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

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

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

regression. The root number can also be selected based on the actual situation.Regular Equation In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $. For example, four groups of property price forecasts: Least Squares $ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regular equations Gradient Descent: Desired stride $ \ Alpha $; Multiple iterations are requ

Original title:Given Arrays a[] and b[], each containing n distinct 2D points in the plane, design a subquadratic algorithm to count The number of points that is contained both in array a[] and array b[].The goal of the topic is to calculate the number of duplicate point, very simple, the code is as follows1 ImportJava.awt.Point;2 Importjava.util.Arrays;3 ImportJava.util.HashSet;4 ImportJava.util.Set;5 6 ImportEdu.princeton.cs.algs4.StdRandom;7 8 Public classplanepoints {9 PrivatesetNewHash

]; - } - System.out.println (arrays.tostring (aux)); the intL = 0; - intR =N; - for(intk = 0; k){ - if(l >= N) Break;//The array of auxiliary elements is exhausted, and the right side of the array does not need to be shifted. + Else if(R>=2*n) array[k]=aux[l++];//all elements of the right element of array are placed in the appropriate position, then simply move the elements of the auxiliary array to the right of the array - Els

} - to Public Static voidMain (string[] args) { + intn = 10; - intN1 =stdrandom.uniform (n); the intN2 = nN1; * int[] A =New int[N1]; $ int[] B =New int[N2];Panax Notoginseng for(inti=0;i){ -A[i] = stdrandom.uniform (100); the } + for(inti=0;i){ AB[i] = stdrandom.uniform (100); the } + Arrays.sort (a); - Arrays.sort (b); $System.out.println ("a=" +arrays.tostring (a)); $System.out.println ("b=" +arrays.tostr

Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no unanimous answer to the definition of machine learning.Arthur Samuel (1959) gives a definition of machine learning:Machine learning is about giving computers the ability to learn without explicit programming.Samuel designed a c