stanford staffing

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

little switch in the Inspector in the right side. That says initial scene.How do we get this thing in navigation controller? Ans:xcode | Editor | Embed in | Navigation controller, so another view Controller's going to appear, a Navigation controller. Notice that it's going to keep the arrow and it moves the arrow to itself. And then it had a little pointer right after It,which was not a segue. This little pointer in between are a special little connection in Xcode, which is the Rootviewcontroll

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

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

be able to find the global optimal solution.When the training sample is very large, each update parameter needs to traverse all the sample calculation total error, so that the learning speed is too slow; this time the random gradient descent algorithm that calculates the error update parameters of a sample is usually more thanThe batch gradient descent method is faster. (Theoretically, there is no guarantee that the random gradient descent can converge)4. For the least squares of linear regress

training set is appropriate.3. No supervised learningExample: In the case of the tumour above, the point in the figure does not know the correct answer, but is from you to find a certain structure, that is, clustering .Applied in the fields of biological genetic engineering, image processing, computer vision, etc.Example: Cocktail party issuesPick up the sounds you're interested in during a noisy cocktail partyUse two different positions to separate the sound from different positionscan also be

Addgesturerecognizer:[[uipinchgesturerecognizer alloc] InitWithTarget:self.faceView action:@ Selector (pinch:)]; //the target here is the handler for this gesture, the Self.faceview .[Self.faceview Addgesturerecognizer:[[uipangesturerecognizer alloc]initwithtarget:self Action: @selector ( Handlehappinessgesture:)]; Self.faceView.dataSource=self;//the controller sets itself as the delegate} //adding gesture recognition to Faceview,faceview will handle gestures through pinch. -(void)

. Summarize the above experimental results:4. The following should be the principle of Li Feifei's Ted speech:5. Some recommendations for working with small datasets:V: Squeezing out of the last few Percent1. Using a small size filter is much better than using a large size filter, and a small size filter can increase the number of non-linearities and reduce the parameters that need to be trained (imagine a 7*7 patch with a 7 The filter convolution of the *7, and the filter convolution of the thr

be trained and predicted immediately, which is called Online learning. each of the previously learned models can do online learning, but given the real-time nature, not every model can be updated in a short time and the next prediction, and the perceptron algorithm is well suited to do online learning:The parameter Update method is: if hθ (x) = y is accurate, the parameter is not updated otherwise, θ:=θ+ yx (in fact, this formula and gradient descent update strategy is the same, but the class l

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 parts:The first part:Supervised Learning supervised learningPart II:Learning theoryPart IV:Unsupervised learningUnsupervised learningClustering algorithmComputer cluster organization, social network analysis, market divisionICA

is that only the input paradigm is provided for this network, and it automatically identifies its potential class rules from those examples. When the study is complete and tested, it can also be applied to new cases. A typical example of unsupervised learning is clustering. The purpose of clustering is to bring together things that are similar, and we do not care what this class is. Therefore, a clustering algorithm usually needs to know how to calculate the similarity to begin to work.

distribution with the mean value of μ 0 and the covariance matrix of Σ, X | y = 1 follows the multivariate Gaussian distribution where the mean value is μ1 and the covariance matrix is Σ (This will be discussed later ). The log function for maximum likelihood estimation is recorded as L (ø, μ 0, μ 1, Σ) = Log 1_mi = 1 p (x (I) | Y (I); μ 0, μ 1, Σ) P (Y (I); ø), our goal is to obtain the parameter ø, μ 0, μ 1, Σ to make L (ø, μ 0, 1, Σ) to obtain the maximum value. The values of the four para

Stanford ml Open Course Notes 15In the previous note, we talked about PCA ). PCA is a direct dimensionality reduction method. It solves feature values and feature vectors and selects feature vectors with larger feature values to achieve dimensionality reduction.This article continues with the topic of PCA, including one application of PCA-LSI (Latent Semantic Indexing, implicit semantic index) and one implementation of PCA-SVD (Singular Value Decompos

hyper-plane (w,b) and the entire training set is defined as:Similar to the function interval, take the smallest geometric interval in the sample.The maximum interval classifier can be regarded as the predecessor of the support vector machine, and is a learning algorithm, which chooses the specific W and b to maximize the geometrical interval. The maximum classification interval is an optimization problem such as the following:That is, the selection of γ,w,b is the maximum gamma, while satisfyin

to return to the previous page. So how to get our action sheet on the screen, using method Presentviewcontroller, the whole method is to make a controller the current controller:Completion is a closure that is called when the Actionsheet renders to the page.The ipad is presented in a popover way.The use of alert is similarWe want to add a text box to the alert by doing the following:Once this method is called, the text box set in the closure will work, where a placeholder is set for the text bo

exposed to nsnotification before, as shown in the example above, receiving this nsnotification in the message center, The nsnotification is then captured in the method thekeyboardappeared of the selector and then extracted from the information in the UserInfo for related processing.Here are some of the other properties of Uitextfield, very interestingFor example, you can set the font size to fit the width of the text box when the text input is many, but the font cannot be reduced indefinitely,

We have a info.plist file in our project, we have been in touch with this file since we did the localization setup, there are many settings.You can even view it in XML format, but usually you edit this file by clicking on the project name at the top of the engineering catalog:You have a lot of features in your app that you can't use until you set them up to enable, and the switch is interesting to indicate whether it's available via a switch, such as when we used the Mapkit to do the map functio

Judgment statement if imageData! = nil{ self.image = UIImage (data:imagedata!) } else { self . Image = Nil}}}}} Note that when calling spinner it is optional, because the page may be generated before the spinner control, which is a common approach when invoking the control. However, this method is loaded with different processing situations, in common with the image, so we choose to stop the gears in the set method that calculates the attribute image:Private var i

, the white split line means that it can be accessed directly, and the yellow split line means that it cannot be accessed directly.We now want to access the properties in a view uiview, we need to use outlet for direct access, if there are certain events in the view (such as click, Swipe) to notify the controller (controllers) need to be accessed by action or delegate way, The model data changes are broadcast to notify the controller.Third, the first objective-c focus and points of attentionWe c