craigslist stanford

)Ans =01Note: The first data above the main diagonal is taken as the starting data, and is sorted in diagonal order as a column vector form4, V = diag (x) returns the element on the main diagonal of matrix X, similar to Diag (X,K), Case 5 of K=0:V=[1 0 0;0 3 0;0 0 3];Diag (v)Ans =133or instead:V=[1 0 3;2 3 1;4 5 3];Diag (v)Ans =133Note: The data of the main diagonal is taken out as a column vector form5,diag (diag (X))Take the diagonal element of the X-matrix and construct a diagonal matrix with

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

Official website: http://see.stanford.edu/see/courseinfo.aspx?coll=824a47e1-135f-4508-a5aa-866adcae11111.JDK installation (select machine corresponding version to download and install)Http://www.oracle.com/technetwork/java/javase/downloads/java-archive-downloads-javase6-419409.html#jdk-6u45-oth-JPRNote: You need to install jdk1.6 and the following version Karel Environment in order to function properly, otherwise the runtime appears JPanel panel only the file option does not Karel the phenomenon

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

1.common Errors (Obob)2.comment comments/* */Paragraph NotesLine Comment3.decomposition "Top Down Design"Decomposition from top to bottom design4.doublebeeper1 ImportStanford.karel.*;2 Public classDoublebeeperextendskarel{3 intNum=0;4 Public voidrun () {5 move ();6 Doublebeeper ();7 Moveback ();8 }9 Public voidMoveback () {Ten turnaround (); One move (); A turnaround (); - } - Public voidturnaround () { the turnleft (); - turnleft (); - } - Public voidDoubleb

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.This course mainly includes three parts: supervised learning algorithm, unsupervised learn

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

The probabilistic graphical model series is explained by Daphne Koller In the probabilistic graphical model of the Stanford open course. Https://class.coursera.org/pgm-2012-002/class/index) Main contents include (reprinted please indicate the original source http://blog.csdn.net/yangliuy) 1. probabilistic Graph Model Representation and deformation of Bayesian Networks and Markov networks. 2. Reasoning and inference methods, including Exact Inference (

A few days ago, Stanford Engineering College launched a free online iOS Application Development Course (CS193P), which focuses on iOS 5 Application Development and is divided into 11 parts, each part consists of a video and a slide. This course is provided through "iCloud, video stream push, and wireless synchronization. To learn this course, you need to know about C and UNIX operating systems. It is best to have object-oriented programming experienc

The probabilistic graphical model series is explained by Daphne Koller In the probabilistic graphical model of the Stanford open course. Https://class.coursera.org/pgm-2012-002/class/index) Main contents include (reprinted please indicate the original source http://blog.csdn.net/yangliuy) 1. probabilistic Graph Model Representation and deformation of Bayesian Networks and Markov networks. 2. Reasoning and inference methods, including Exact Infer

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