stanford cybersecurity

{ @IBOutlet weak var display:uilabel! var Userisinthemiddleoftypinganumber:bool = False @IBAction func appenddigit (Sender:uibutton) {let digit = sender.currenttitle! println ("digit = \ (digit)") if Userisinthemiddleoftypinganumber { display.text = display.text! + digit } else { display.text = digit Userisinthemiddleoftypinganumber = True } } }Operating effect:OK, this is the point here, we continue to speak

,....} (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

The second lesson of the Stanford iOS Development video was finished tonight, and the professor's homework was done.The professor's assignment is to expand on the existing card game app, so that random colors appear at each flop.In fact, this is very simple, that is, the class written by the Professor class to instantiate, while acquiring the specific contents of the card, that is, the contents attribute.Below I will put the finished app and app class

NSObject object, and the value is anyobject.then int, float, double, bool are all received from the NSNumber Bridge, NSNumber is OC in all about the value of the object, int, float, double these and C language int, float, double is also bridged good, So if the API has a C-language int parameter, it can also accept an int from SwiftOf course, if you are sure what data type you need, you can do type conversion. For example, length is a property of NSString, does not exist in string, it does not e

Uikitclass Viewcontroller:uiviewcontroller { @IBOutlet weak var display:uilabel! var Userisinthemiddleoftypinganumber:bool = False @IBAction func appenddigit (Sender:uibutton) {let digit = sender.currenttitle! println ("digit = \ (digit)") if Userisinthemiddleoftypinganumber { display.text = display.text! + digit } else { display.text = digit Userisinthemiddleoftypinganumber = True } } }Operating effect:OK, t

out that my mother had never graduated from college and that my father had never graduated from high school. She refused signFinal adoption papers. she only relented a few months later when my parents promised that I wowould someday go to college. and 17 years later I did go to college. but I naively chose a college that was almost as expensive as Stanford, and all of my working-classParents 'savings were being spent on my college tuition. after six

) bool userisinthemiddleofenteringanumber; // The initial value is 0 and belongs to the private @ property (nonatomic, strong) calculatorbrain * brain; @ end Most of the private implementation methods are the target action methods implemented by the view and controller: @implementation CalculatorViewController- (IBAction)digitPressed:(UIButton *)sender;- (IBAction)OperationPressed:(UIButton *)sender;- (IBAction)enterPressed;@end 3) view Enter "56 enter 3 +", and the r

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 (

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 machine learning.

From ⅱ to IV, linear regression is used. Chapter II describes simple linear regression (SLR) (single variable ), chapter III describes the basis of line generation, and chapter IV describes multivariate regression (greater than one independent variable ). The purpose of this article is to implement some algorithms that appear in chapter II. Suitable for scholars who have already completed Stanford courses in this chapter. I am just a beginner and tr

(Theta0_vals) for j = 1:length (theta1_vals) t = [Theta0_vals (i); Theta1_vals (j)]; J_vals (I,J) = SUM (sum ((x*t-y). ^2,2), 1)/(2*length (y)); endend% Plot the surface plot% Because of the The-meshgrids work in the surf command, we need to% transpose j_vals before Calling surf, or else the axes would be flippedj_vals = J_vals ' Figure;surf (theta0_vals, Theta1_vals, j_vals); axis ([-3, 3, -1, 1,0,40]); Xlabel (' \theta_0 '); Ylabel (' \theta_1 ') endPerform:X=load (' Ex2x.da

Standford Named entities Recognizer (NER), named entity recognition is a subtask of information extraction (information Extraction), which locates and classifies the atomic elements of the text (Atomic element). Then output to a fixed-format directory, such as: Person name, organization, location, time representation, quantity, currency value, percentage, and so on. Official website (http://nlp.stanford.edu/ner/)The NER contains the following model: 3 class model:location, person, Organiza

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

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