stanford university machine learning coursera

calculates the accuracy of the entire system at this time: As shown in, text recognition consists of four parts. Now we can find the system accuracy after optimization for each part. The question is, how can we improve the accuracy of the entire system? We can see from the table that, if we have optimized the text moderation part, the accuracy will be72%Add89%If we optimize the character segmentation, the accuracy is only from89%To90%If character recognition is optimized90%To100%In contr

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounte

An introductory tutorial on machine learning with a higher degree of identity, by Andrew Ng of Stanford. NetEase public class with Chinese and English subtitles teaching video resources (http://open.163.com/special/opencourse/ machinelearning.html), handout stamp here: http://cs229.stanford.edu/materials.htmlThere are a variety of similar course

It is decided that machine learning is under system learning, and Stanford courseware is the main line. Notes1 is part of the http://www.stanford.edu/class/cs229/notes/cs229-notes1.pdf about Regression 1. Linear Regression For example, if the House Price is predicted and the data cannot be found on the Internet, use

invoking the example in MATLAB above, we can define the cost function of the logistic regression as follows:In the figure, Jval represents the cost function expression, where the last item is the penalty for the parameter θ; The following is a gradient of the derivation of each θj, where θ0 is not in the penalty, so gradient is not changed, and Θ1~θn has one more (λ/m) *θj respectively;At this point, regularization can solve the linear and logistic overfitting regression problem ~

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.

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

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

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