stanford cybersecurity

been in touch with Nsnotification before. As seen in the example above, receive this nsnotification in the message center, then capture the nsnotification in the corresponding method thekeyboardappeared of the selector and then extract the information from the UserInfo for related processing.Here are some of the other properties of Uitextfield, which are interestingFor example, you can set the font size to fit the width of the text box when there is a lot of text input. However, this font can n

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 Class machine learning: Advice for applying mac

An introductory tutorial on machine learning with a higher degree of identity, by Andrew Ng of Stanford. NetEase public class with Chinese and English subtitles teaching video resources (http://open.163.com/special/opencourse/ machinelearning.html), handout stamp here: http://cs229.stanford.edu/materials.htmlThere are a variety of similar course learning notes on the Web, which will also be part of my study. Be patient and be curious~The first section

Tag:gpo represents nodes relationships info nodsrcbspnbsp collection; Treegraphnode TSN = Gs.root (); for (typeddependency I:tdl) {Reln represents the relationship of a node, and DEP represents the node to which the dependency is directedif (i.reln () = = Grammaticalrelation. ROOT) {Log.info ("Output root:" + I.DEP (). toString ());;}}Stanford NLP 3.8.0 Parse to get the root node through a Java program

PDF documents for the Open Class series have been uploaded to Csdn resources, please click here to download. This article corresponds to the 12th video of the Stanford ML Public course, and the 12th video is not very relevant to the previous one, opening a new topic-unsupervised learning. The main contents include the K-means clustering (K-means) algorithm in unsupervised learning, the mixed Gaussian distribution model (Mixture of Gaussians, MoG), th

2011 Winter Stanford University Open course iOS App development Tutorial is a very classic tutorial, the old man is very good at speaking. Take notes to summarize.First lesson name: MVC and Introduction to objective-c the main contents of this lesson are:Overview of iOS-what is iOSMVC-Object-oriented conceptsobjective-c-Introduction to the concept of languageiOS includes four layers of coresThe kernel is the Mach 4.x BSD Unix kernel Mac OS 10 operatin

I admire Apple's CEO Steve Jobs, a dreamer. This is Steve Jobs's speech at the Stanford University graduation ceremony. There is a reason for everything.-- When we occasionally stop looking back on our journey, we may be surprised by the elaborate design of fate, so we feel that these experiences have affected my life. Find your favorite. Treat every day as the end of lifeIf you are hungry for knowledge, if you are foolish-stay hungry, Stay foolish.

Stanford recommended Reading directory Stanford Deep Learning Web site recommended reading directory: UFLDL Recommended ReadingsIf you ' re learning about UFLDL (unsupervised Feature learning and deep learning), this is a list of the papers to consider Rea Ding. We ' re assuming you ' re already familiar and basic machine learning at the level of [CS229 (lecture notes available)].The Basics: [cs294

ObjectiveThe most famous tutorial on iphone development is the "Open iphone Development Course" released by Stanford University. This public course, formerly known as the IPhone Development tutorial, was introduced this year due to the popularity of tablets, and has also been added to the ipad development-related curriculum. In the NetEase open class, there is a 2010-year video of the tutorial, and the front 15 set with Chinese subtitle files, very su

Second lecture: Simple word vector representation: Word2vec, Glove (easy word vector representations:word2vec, Glove)Reprint please specify the source and retention link "I love Natural Language processing": http://www.52nlp.cnThis article link address: Stanford University deep Learning and Natural language processing second: Word vectorRecommended Reading materials: paper1:[distributed representations of Words and phrases and their compositi

It should be this time last year, I started to get into the knowledge of machine learning, then the introductory book is "Introduction to data mining." Swallowed read the various well-known classifiers: Decision Tree, naive Bayesian, SVM, neural network, random forest and so on; In addition, more serious review of statistics, learning the linear regression, but also through Orange, SPSS, R to do some classification prediction work. But the external said that they are engaged in machine learning

The days of Stanford 1. Here: From an ignorant me, to now I have been able to understand programming ideas, programming languages, and the pain points of programmers in general. All this is what Stanford gave me. I don't know how to thank it for its existence. because of it, I can be closer to my dream. As I first came to college, I knew nothing about the programming world. I only knew the power of a softwa

Steve Jobs can be regarded as a legend of Apple's myth. Once I saw the legend of apple, I think Steve's experience is a legend, So I admire him more and more. So here is a speech he gave at Stanford. I am honored to be with you today at your commencement from one of the finest universities in the world. I never graduated from college. truth be told, this is the closest I 've ever gotten to a college graduation. today I want to tell you three stories f

that the view is still in the hierarchical structure, but does not display or respond. Clicking it is like it is not in the view, but it is actually there. Hidden is quite commonly used. For example, if a view is not needed on a small screen for the time being, it is hidden first. You can hide a view completely by setting hidden property: @property (nonatomic) BOOL hidden; myview.hidden = YES;//view will not be on screen and will not handle events The hidden overhead is less transparent, but t

ObjectiveFor deep learning, novice I recommend to see UFLDL first, do not do assignment words, one or two nights can be read. After all, convolution, pooling what is not a particularly mysterious thing. The course is concise, sharply, and points out the most basic and important points.cs231n This is a complete course, the content is a bit more, although the course is computer vision, but 80% is the content of deep learning. The work of the image is not available for the time being, I will skip i

One point cloud data is the Stanford Rabbit, do three-dimensional point cloud reconstruction after the person can take a good look, but simply read into the program, as a beginner better, further research is still behind, there is a chance that will continue to upload, for example, Delauny triangular grid division, but also want to further study the snake curve: http://download.csdn.net/detail/u013467442/8847911function Point_cloud ()CLCA=importdata (

Stanford ml Public Lesson Note 15In our last note we talked about PCA (principal component analysis).PCA is a kind of direct dimensionality reduction method. By solving eigenvalues and eigenvectors, and selecting some characteristic vectors with large eigenvalues, the effect of dimensionality reduction is achieved.This paper continues the topic of PCA, which contains an application of PCA--lsi (latent Semantic indexing, implied semantic index) and an

This period of time in learning Stanford iOS 8 teaching video, learning without thinking is idle, so prepare to summarize the video to learn some notes, so that they can deepen their understanding.Now I have learned 6 lessons, from these six lessons, the first section of the lecture is mainly about a calculator demo, and interspersed with a few iOS introduction, as well as the introduction of MVC. The fourth section mainly introduces some swift syntax

I. Introduction of the CourseStanford University launched an online natural language processing course in Coursera in March 2012, taught by the NLP field Daniel Dan Jurafsky and Chirs Manning:https://class.coursera.org/nlp/The following is the course of the study notes, to the main course ppt/pdf, supplemented by other reference materials, into the personal development, annotation, and welcome everyone in the "I love the public class" on the study together.Courseware Summary: The

(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