stanford machine learning certificate

Since the end of last year to learn Andrew Ng's machine learning public class, in accordance with its courseware to try to achieve some of the algorithm to deepen understanding, but in this process encountered some problems, or for the implementation of the program, or to understand the algorithm. So prepare to organize this course and document your understanding, either right or wrong, to discuss together.

It should be this time last year, I started to get into the knowledge of machine learning, then the introductory book is "Introduction to data mining." Swallowed read the various well-known classifiers: Decision Tree, naive Bayesian, SVM, neural network, random forest and so on; In addition, more serious review of statistics, learning the linear regression, but a

function and the derivation of each parameter when using it. we implement the costfunction ourselves and pass in the response parameter. We can return the following two values at a time: For example, call the fminunc () function and use @ to input the pointer to the costfunction function. For the initialized Theta, you can also add options (gradobj = on indicates "Open the gradient target parameter ", that is, we will provide gradient parameters for this function ): 6.7 multi-category classifi

is more than one, the Newton method iterates over the rule:Newton's method usually has a faster convergence rate than the batch gradient, and it takes a much smaller number of iterations to get close to the minimum value. However, when the parameters of the model are many (n), the computational cost of the Hessian matrix will be large, resulting in a slower convergence rate, but when the number of arguments is not long, the Newton method is usually much faster than the gradient descent.Summariz

based on the minimum mean variance. The closer to the predicted point, the heavier the weight, which is to use the points near the check to give higher weights. The most common is the Gaussian nucleus. The weights corresponding to the Gaussian nuclei are as follows:In (Formula 2), the only thing we need to make sure is that it's a user-specified parameter that determines how much weight is given to nearby points.Therefore, as shown in (Equation 3), local weighted linear regression is a non-para

symmetric semi-definite matrixin the case where the data is non-linear:called L1 norm soft margin SVM. is a convex optimization problem. It allows an interval of less than 1, which allows for the categorization of errors. SMO algorithm:coordinate ascent algorithm:This algorithm has more iterations, but at some point the inner loop will be very fast if a parameter in W (A1,,, am) is very small at the cost of finding the optimal value. SMO:If only one α is solved as SVM, the other α is fixed. obt

endfunction Initializes the matrix for the preceding dataset. Call a function to calculate the value of the cost function. 1> X = [1 1; 1 2; 1 3]; 2> Y = [1; 2; 3]; 3> Theta = [0; 1]; % records is 0, 1 h (x) = x. The value of the cost function is 04> J = costfunctionj (X, Y, theta) 5 J = 0. 1> Theta = [0; 0]; % values is 0, 0 h (x) = 0. data cannot be fitted at this time. 2> J = costfunctionj (X, Y, theta) 3 J = 2.33334 5> (1 ^ 2 + 2 ^ 2 + 3 ^ 2)/(2*3) % value of the cost function 6 ans = 2

Machine learning defines learning definitionArthur Samuel (1959). Machine Learning:field of study, gives computers the ability to learn without being explicitly programmed.There is no clear programming case to make the computer capable of learning the field of study.Four par

regression as shown below, (note that in matlab the vector subscript starts at 1, so the theta0 should be theta (1)).MATLAB implementation of the logistic regression the function code is as follows:function[J, Grad] =Costfunctionreg (Theta, X, y, Lambda)%costfunctionreg Compute Cost andgradient for logistic regression with regularization% J=Costfunctionreg (Theta, X, y, Lambda) computes the cost of using% theta as the parameter for regularized logistic re Gression andthe% Gradient of the cost w

,....} (A is the 1th word in the dictionary and Nip is the No. 35000 Word). So for naive Bayes, it can be expressed as the following matrix (the 1th element of the matrix is 1, and the No. 35000 element is also 1)in the multinomial event model, it is expressed as,. This means that the 1th word of the message is a, and the No. 35000 Word is nip. In this case, if the 3rd word in the message is a, the naive is unchanged, but the representation in the Multinomial event model will be x3=1. This allow

algorithm solves the problem of large optimization by decomposing it into several small optimization problems. These small optimization problems are often easy to solve, and the results of sequential solution are consistent with the results of solving them as a whole.The SMO works based on the coordinate ascent algorithm.1, coordinate ascentAssume that the optimization problem is:We select one of the parameters in turn to optimize this parameter, which causes the W function to grow fastest.The

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

Terryj.sejnowski. (c) function interval and geometric interval of support vector machineto understand support vector machines (vectormachine), you must first understand the function interval and the geometry interval. Assume that the dataset is linearly divided. first change the symbol, the category y desirable value from {0,1} to { -1,1}, assuming that the function g is:The objective function H also consists of:Into:wherein, Equation 15 x,θεRn+1, and X0=1. In Equation 16, x,ωεRN,b replaces the

returnWhen the classification problem is no longer two yuan but K yuan, that is, y∈{1,2,..., k}. We can solve this classification problem by constructing the generalized linear model. The following steps are described.Suppose y obeys exponential family distribution, φi = P (y = i;φ) and known. So. We also define.Also 1{} The condition for the representation in parentheses is the true value of the entire equation is 1, otherwise 0. So (T (y)) i = 1{y = i}. From the knowledge of probability theor

equal to 0.3. Optimal interval classifierThe optimal interval classifier can be defined asSo set its limit toSo its LaGrand day operator isThe derivation of its factors is obtained by:ObtainedIt is possible to differentiate its factor B by:The (9) type (8) can beAnd then by the (10) type of generationSo the dual optimization problem can be expressed as:The problem of dual optimization can be obtained, so that the Jiewei of B can be obtained by (9).For a new data point x, you can make prediction

Stanford cs231n 2017 newest Course: Li Feifei Detailed framework realization and comparison of depth learning by Zhuzhibosmith June 19, 2017 13:37 Stanford University Course cs231n (convolutional Neural Networks for visual recognition) is widely admired in academia as an important foundation course in depth learning an

ObjectiveFor deep learning, novice I recommend to see UFLDL first, do not do assignment words, one or two nights can be read. After all, convolution, pooling what is not a particularly mysterious thing. The course is concise, sharply, and points out the most basic and important points.cs231n This is a complete course, the content is a bit more, although the course is computer vision, but 80% is the content of deep

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is a library that recognizes and standardizes time expressions. Stanford spied-Use patterns on the seed set to iteratively learn character entities from untagged text Stanford Topic Modeling toolbox-is a topic modeling tool for social scientists and other people who want to analyze datasets. Twitter text Java-java Implementation of the tweet processing library Mallet-Java-based statistical