stanford courses

input x and the training sample X.In SVM feature space, because the dimension of training samples may be very high, the kernel method can efficiently calculate the representation of the inner product, but only for some specific feature spaces.Explore the entire SVM calculation process, all the steps can not directly calculate x (i), and by calculating the inner product of the eigenvector to obtain results, so the kernel method is introduced.Another property of the algorithm is that, since the s

{stack = [Program mutablecopy]; // The local variable stack does two things for it using Introspection: first, make sure it is an array, second, you have made a variable copy, so you can eat it} // because the runprogram implementation method is to use recursion to digest everything on the stack, and it must be variable. Digest. (Recursion means loop until the final condition is true. The final condition is an empty array or a result is obtained .) Stack is static, while mutablecopy returns ID.

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/test sets (Model selection and training/verification/test Set) 4) Diagnosing bias vs. variance (diagnostic deviation and variance) 5) Regularization and bias/variance (Regularization

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

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

the value is, the closer the value of the evaluation function is to the midline position of the parabolic curve, that is, the closer it is to the minimum value. It can be represented by an example: Let's take a look at the meaning. When the value is too small, the update is slow, and the gradient descent algorithm will slow down in execution. When the value is too large, the gradient descent algorithm may exceed the target value (minimum value), leading to non-convergence, even divergence. As

Reprinted please indicate the source Author: Pony I recently watched the I can recall how I spoke C or C ++ when I was studying at school. I remember that I had hardly ever heard of a few lines of code from my teacher. I read them on PPT. Stanford teaches how to use C to implement a general linear lookup function. The so-called generic function is to find any type of data. in C ++, templates can be used. If C is used for implementation, pointers ca

a UIView) by translation.x and translation.y// for example, if I were a graph and my origin was set by an @property called origin self.origin = CGPointMake(self.origin.x+translation.x,self.origin.y+translation.y); [recognizer setTranslation:CGPointZero inView:self];//Here we are resetting the cumulative distance to zero. }}//Now each time this is called,we‘ll get the "incremental" movement of the gesture(Which is what we want).//If we wanted the "cumulative" movement of the gesture

.12. " Push "is the kind of segue if the" the "and" the "controllers is inside a uinavigationcontroller.13.sometimes it make s sense to segue directly when a button was touched,but not always. For Example,what if want to conditionally segue? You can programmatically invoke segues using method in Uiviewcontroller:-(void) Performseguewithidentifier: (NSString *) Segueid Sender: (ID) sender; The Segueid is Set inchThe Attributes Inspectorinchxcode.the Sender isThe initiator of the segue (a UIButton

be Sen T to the target (the Pannableview) during the handling of the recognition of this gesture. If we don ' t do this,then even though the Pannableview implements Pan:,it would never get called because we would has Neve R added this gesture recognizer to the view's list of gestures that it recognizes. Think of this as "turning tHe handling of this gesture on ". Only UIView instances can recognize a gesture (because uiviews handle all touch input). But Ant object can tell a UIView to recognize

been in touch with Nsnotification before. As seen in the example above, receive this nsnotification in the message center, then capture the nsnotification in the corresponding method thekeyboardappeared of the selector and then extract the information from the UserInfo for related processing.Here are some of the other properties of Uitextfield, which are interestingFor example, you can set the font size to fit the width of the text box when there is a lot of text input. However, this font can n

assumptions tend to be 0, but the actual labels are 1, both of which indicate a miscarriage of judgment. Otherwise, we define the error value as 0, at which point the value is assumed to correctly classify the sample Y.Then, we can use the error rate errors to define the test error, that is, 1/mtest times the error rate errors of H (i) (xtest) and Y (i) (sum from I=1 to Mtest).Stanford University public Class machine learning: Advice for applying mac

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 learning notes on the Web, which will also be part of my study. Be patient and be curious~The first section

Tag:gpo represents nodes relationships info nodsrcbspnbsp collection; Treegraphnode TSN = Gs.root (); for (typeddependency I:tdl) {Reln represents the relationship of a node, and DEP represents the node to which the dependency is directedif (i.reln () = = Grammaticalrelation. ROOT) {Log.info ("Output root:" + I.DEP (). toString ());;}}Stanford NLP 3.8.0 Parse to get the root node through a Java program

PDF documents for the Open Class series have been uploaded to Csdn resources, please click here to download. This article corresponds to the 12th video of the Stanford ML Public course, and the 12th video is not very relevant to the previous one, opening a new topic-unsupervised learning. The main contents include the K-means clustering (K-means) algorithm in unsupervised learning, the mixed Gaussian distribution model (Mixture of Gaussians, MoG), th

Linux Basics Introductory Course: HTTPS://WWW.SHIYANLOU.COM/COURSES/1A very good Linux basic course, refined, concise! Recommended!Course Content: 1th Introduction to Linux System https://www.shiyanlou.com/courses/1/labs/1/document section 2nd Basic concepts and Operation https:// www.shiyanlou.com/courses/1/labs/2/document 3rd section User and file Rights Manage

Second lecture: Simple word vector representation: Word2vec, Glove (easy word vector representations:word2vec, Glove)Reprint please specify the source and retention link "I love Natural Language processing": http://www.52nlp.cnThis article link address: Stanford University deep Learning and Natural language processing second: Word vectorRecommended Reading materials: paper1:[distributed representations of Words and phrases and their compositi

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 also through Orange, SPSS, R to do some classification prediction work. But the external said that they are engaged in machine learning

The days of Stanford 1. Here: From an ignorant me, to now I have been able to understand programming ideas, programming languages, and the pain points of programmers in general. All this is what Stanford gave me. I don't know how to thank it for its existence. because of it, I can be closer to my dream. As I first came to college, I knew nothing about the programming world. I only knew the power of a softwa

Steve Jobs can be regarded as a legend of Apple's myth. Once I saw the legend of apple, I think Steve's experience is a legend, So I admire him more and more. So here is a speech he gave at Stanford. I am honored to be with you today at your commencement from one of the finest universities in the world. I never graduated from college. truth be told, this is the closest I 've ever gotten to a college graduation. today I want to tell you three stories f