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Stanford University Machine Learning public Class (II): Supervised learning application and gradient descent

mathematical expression was unfolded using Taylor's formula, and looked a bit ugly, so we compared the Taylor expansion in the case of a one-dimensional argument.You know what's going on with the Taylor expansion in multidimensional situations.in the [1] type, the higher order infinitesimal can be ignored, so the [1] type is taken to the minimum value,should maketake the minimum-this is the dot product (quantity product) of two vectors, and in what case is the value minimal? look at the two vec

Deep Learning III: PCA in 2d_exercise (Stanford University UFLDL in depth learning tutorial)

)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

Stanford University: the NO. 0 Session of programming methodology Karel Environment construction

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

Stanford University: Programming Methodology Lesson Three

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

Coursera open course notes: "Advice for applying machine learning", 10 class of machine learning at Stanford University )"

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) Reg

[Lecture Notes] Stanford University Open class: IOS 7 App Development Lecture6

.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

[Lecture Notes] Stanford University Open class: IOS 7 App Development Lecture7

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

Stanford University public Class machine learning: Advice for applying machines learning-evaluatin a phpothesis (how to evaluate the assumptions given by the learning algorithm and how to prevent overfitting or lack of fit)

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 Cl

The second course of natural language processing, Stanford University, "Text Processing basics (Basic text Processing)"

(normalization): It mainly includes capitalization conversion, stemming, simplified conversion and so on. Segmentation (sentence segmentation and decision Trees): Like!? Such symbols are clearly divided in meaning, but in English. " "will be used in a variety of scenarios, such as the abbreviation" INC "," Dr ",". 2% "," 4.3 "and so on, can not be processed by simple regular expression, we introduced the decision tree classification method to determine whether th

Stanford University Machine Learning public Class (VI): Naïve Bayesian polynomial model, neural network, SVM preliminary

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

Machine Learning Stanford University Open Class (1)

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

[Lecture Notes] Stanford University Open class: IOS 7 App Development Lecture5

) Methodtoinvokeifsomethinghappensname: (NSString*) name//Name of station (a constant somewhere)Object:(ID) sender;//whose changes you ' re instertsted in (Nil is anyone ' s)//you 'll then being notified when there is broadcasts-(void) Methodtoinvokeifsomethinghappens: (Nsnotification *) notification{Notification.name//The name passed aboveNotification.Object //The object sending you the notificationNotification.userinfo//notification-specific Information about what happened} 10.Be sure to ' tun

Stanford University Open Class: IOS 7 App Development Lecture11

it via the Uitableviewcontroller @property (strong) Uirefreshcontrol *refreshcontrol; Start it with...-(void) beginrefreshing;Stop it with...-(void) endrefreshing; 11.What If your Model changes?-(void) reloaddata;Causes the table view to call Numberofsectionsintableview:and Numberofrowsinsection:all over again and then Cellforrowat Indexpath:on each visible cell. Relatively heavyweight,but if your entire data structure Changes,that ' s what you need. If only part of your Model Changes,there is

Stanford University public Class machine learning: Advice for applying machines learning | Deciding what to try Next (Revisited) (for high-deviation, high-variance resolution and the choice of hidden layers)

default is to use a hidden layer is a reasonable choice, but if you want to choose the most appropriate layer of hidden layer, you can also try to split the data into training sets, validation sets and test sets, and then try to use a hidden layer of neural network to train the model. Then try two, three hidden layers, and so on. Then see which neural network behaves best on the cross-validation set. That means you get three neural network models, one, two, and three hidden layers, respectively

Coursera University program design and algorithm special courses perfect coverage

#include using namespacestd;/*int Wanmeifugai (int n) {if (n%2) {return 0; } else if (n==2) {return 3; }else if (n = = 0) return 1; else return (3*3) *wanmeifugai (n-4);}*///The following is a reference to the online program/*Ideas: Citation:http://m.blog.csdn.net/blog/njukingway/20451825First: F (n) = 3*f (n-2) + ... f (n) = 3*f (n-2) + 2*f (n-4) +....//just now our recursion is pushed in the smallest unit (3 blocks), but there are large units of small units (6, 9, 12 blocks, etc.) There

Johns Hopkins University series of data Science courses--r language: Data types

valuesIs.na () is used to test whether the object is Na,is.nan () to test whether the object is Nan. Na is Nan, but Nan is not na,nan much deeper than NA.10. Data frameThe data frame is used to store tabular data and is created with Data.frame (). You can treat a data frame as a special list collection, with the same length for each list and the same data type for the corresponding location.11. NamingR can be named after the object by the function names (), by Dimnames () to the matrix, or by n

Johns Hopkins University series of data Science courses--r language: extracting subsets

TRUEFALSE TRUEFALSE TRUE> x[good][11245Complete.cases () can also handle matrix data> airquality[1:6,] Ozone SOLAR.R Wind TempMonth Day1 A the 7.4 the 5 12 $ 118 8.0 the 5 23 A 149 12.6 About 5 34 - 313 11.5 + 5 45Na Na14.3 About 5 56 -NA14.9 the 5 6> Airquality[good, [1:6,] Ozone SOLAR.R Wind TempMonth Day1 A the 7.4 the 5 12 $ 118 8.0 the 5 23 A 149 12

Introduction to GSIs master's courses at South Korea's yanshi University

Yanshi University is one of the most famous universities in South Korea. GSIS courses are provided by the Graduate School of International Studies in international trade, finance, and international relations, master's and doctoral courses in English, such as regional studies, and a wealth of scholarships, especially for Chinese students. Entry Qualifications Entr

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