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
Last night, my roommate White gave me an article mactalk the public, the author said no longer can not find such a speech, so full of passion, so give us a striking revelation, stay hungry, stay foolish, also become my next work life faith,If you are hungry, be foolish !I specifically found this Chinese-English version of Steve Jobs at Stanford graduate speech, give us a lesson, give us some enlightenment.=============================================
: Http://nlp.stanford.edu/software/lex-parser.shtml#Download
Create a project, decompress the downloaded package, and associate the stanford-parser.jar and stanford-parser-2012-07-09-models.jar in the project. The stanford-parser-2012-07-09-models.jar is the language model file, and the date varies by version.
Put parserdemo. Java in the decompressed folder t
(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
,....} (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
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
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
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.