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[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

(note) Stanford machine Learning--generating learning algorithms

two classification problem, so the model is modeled as Bernoulli distributionIn the case of a given Y, naive Bayes assumes that each word appears to be independent of each other, and that each word appears to be a two classification problem, that is, it is also modeled as a Bernoulli distribution.In the GDA model, it is assumed that we are still dealing with a two classification problem, and that the models are still modeled as Bernoulli distributions.In the case of a given y, the value of x is

Stanford iOS Development Lesson Five (Part II)

)) { Cgpoint translation = [gesture translationinview:self.faceview];//conversion to point displacement changes in the coordinate system self.happiness-= TRANSLATION.Y/2; In addition to the effect of 2. Decrease the amplitude of the change [gesture Settranslation:cgpointzero inview:self.faceview];//0 to make the range of changes not additive }}Next, implement the functions defined in the Protocol,-(float) Smileforfaceview: (Faceview *) sender{ return (self.happiness

"We all love Paul Hegarty." Stanford IOS8 public class personal note 3 Xcode, Auto layout, and MVC

. The data source does not deal with such things as would and should, he answers how many songs there are and returns the quantity to the view. The view now opens up space for these 10,000 songs. So the function of the controller (C) is to interpret and format the data provided by these models (M) for the View (V).So the question comes again, can the model communicate with the controller? Obviously not. But suppose the data changes how to notify our controllers? It still uses this method of blin

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

Notes of machine Learning (Stanford), Week 6, Advice for applying machine learning

are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508 Training errors, cross-validation errors, and relationships between lambda graphs are represented as follows:When th

Machine Learning-Stanford: Learning Note 5-generating learning algorithms

unreasonable. That is, in the past two months the word has not appeared in the mail, it is considered that the probability of 0, unreasonable.Generally speaking, it is unreasonable to think that these events will not happen if they have not been seen before . Solve this problem with Laplace smoothing.4. Laplace SmoothingAccording to the maximum likelihood estimate, p (y=1) = # "1" s/(# "0" s + # "1" s), that is, the probability of Y being 1 is the ratio of the number of 1 in the sample to all s

[Stanford] MVC Introduction

Model,view no model-oriented broadcasts, and the view and controller will broadcast to each other.The model broadcast is very useful because it is not visible, but there are restrictions that can only notify the object that is allowed to notify what happened.(7) 1 model only 1 controllers.Can a controller have a view conversation with someone else? Normally the controller will have a pointer pointing to another controller as the view, which will require the controller to display the object. So,

Stanford University public Class machine learning: Machines Learning System Design | Data for machine learning (the learning algorithm behaves better when the volume is large)

For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very

Stanford UFLDL Tutorial MATLAB Modules_stanford

Matlab Modules matlab Modules Sparse Autoencoder |sparseae_exercise.zip Checknumericalgradient.m-makes sure that Computenumericalgradient is implmented correctly computenumericalgradient.m-computes numerical gradient of a function (to is filled in)

Stanford UFLDL Tutorial exercise:learning color features with Sparse Autoencoders_stanford

Exercise:learning color features with Sparse autoencoders Contents [hide] 1Learning color features with Sparse Autoencoder s 1.1Dependencies 1.2Learning from color image patches 1.3Step 0:initialization 1.4Step 1:modify your sparse Autoencoder To

Stanford UFLDL Tutorial Using reverse conduction thought to take the derivative _stanford

Derivation of Contents with reverse conduction thought [hide] 1 Introduction 2 Example 2.1 Example 1: target function of weight matrix in sparse coding 2.2 Example 2: Smooth terrain in sparse coding L1 sparse penalty Function 2.3 example 3:ica

Stanford University CS231 Course notes 2_localiza

Cnn CV Tasks Classification Classification + Localization CLASSIFICATION:C classesInput:imageOutput:class LabelEvaluation Metric:accuracyLocalizationInput:imageOutput:box in the image (X,y,w,h)Evaluation Metric:intersection over Union method one:

Stanford Machine Learning---The sixth lecture. How to choose machine Learning method, System _ Machine learning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines

Stanford UFLDL Tutorial Exercise:sparse Coding

Exercise:sparse Coding Contents [Hide] 1Sparse Coding 1.1Dependencies 1.2Step 0:initialization 1.3Step 1:sample patches 1.4Step 2:implement and check S Parse coding cost functions 1.5Step 3:iterative optimization Sparse Coding

Stanford iOS Development Lesson Five (Part One)

Reprint please indicate the sourcehttp://blog.csdn.net/pony_maggie/article/details/27706991Author: PonyBecause the content of lesson five is more, it is divided into two parts to write.Basic operation of a screen rotationControls whether the current

Stanford iOS Development Lesson Five (Part II)

Reprint please indicate the sourcehttp://blog.csdn.net/pony_maggie/article/details/27845257Author: PonyFive code examplesThe above mentioned knowledge points are covered in this example. In addition, I'm just here to analyze some important code,

Stanford iOS Handout Courseware Summary II

The number of parameters in the 1,oc is different, it can be two completely different methods. Such as-(void) Addcard: (card *) Card attop: (BOOL) attop; -(void) Addcard: (card *) card; A second method can be implemented-(void) Addcard: (Card *)

Stanford iOS7 Open Class 4-6 notes and demo demo

1. Variable type do not misuse the ID, if it is not carefully easy to throw an error in the execution of the program, because in the compilation phase the compiler simply detects that the variable object belongs to the type, especially when the type

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