Sometimes we need to collect some classic things, always aftertaste, and Coursera on some of the courses is undoubtedly classic. Most of the completed courses in Coursera provide a complete set of teaching resources, including PPT, video and subtitles, which will be very easy to learn when offline. It is obvious that we will not go to a file to download a file, Only fools do so, programmers are smart!
What
of:sum(dat$Zip*dat$Ext,na.rm=T)(Original data Source:http://catalog.data.gov/dataset/natural-gas-acquisition-program)Question 4Read the XML data on Baltimore restaurants from here:Https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Frestaurants.xmlHow many restaurants has zipcode 21231?Question 5The American Community Survey distributes downloadable data about the states communities. Download The 2006 microdata survey about housing for the state of Idaho using Download.file () from here:Https
1. What is a special course (specializations)?If you want to learn a major that you do not understand, you can study according to the special course arrangement. Coursera Special Course collects a field of curriculum, and according to the Order of teaching, it is very suitable for the new people who don't feel well.2. Program Design and algorithmThis special course is a computer Foundation course published by Peking University in
(Datasets) data (IRIS)#Exploratory Analysisnames (Iris) head (IRIS)#The following attempts to take Virginica,speal. The method of length is all wrongiris[,2]iris[iris$species=="virginica", 2]mean (iris[iris$species=="virginica", 2])##the above is Error,not correct##tapply (Test$sepal.length,test$species,mean)#using Species.mean to group vectors, this method is feasible, but the above method is necessary to look at the errorLibrary (Datasets) data (Mtcars) #以下为做某个题时的若干测试. And a trial-and-error l
networks and overfitting:
The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ):
It has a low computing cost.
The following is a "big" Neural Network (which has many parameters and is easy to overfit ):
It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method.
References:
Machine Learning video can be viewed or downloaded on Coursera
NTU-Coursera ml: HomeWork 1 Q15-20Question15
The training data format is as follows:
The input has four dimensions, and the output is {-1, + 1 }. There are a total of 400 data records.
The question requires that the weight vector element be initialized to 0, and then "Naive Cycle" is used to traverse the training set. When the iteration is stopped, the weight vector is updated several times.
The so-called "Naive Cycle" means that after an error i
This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA
Download coursera-Wunda-Machine learning-all programming practice answers
In this exercise, you will implement the K-means clustering algorithm and apply it to compressed images. In the second section, y
Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introducedThe composition of the threadImplementation of threading mechanismUser-level threads, c
full implementation of multi-layered neural network recognition picture of the cat Original Coursera Course homepage, in the NetEase cloud classroom also has the curriculum resources but no programming practice. This program uses the functions completed in the last job, fully implementing a multilayer neural network, and training to identify whether there is a cat in the picture. There is no comment in the Code and Training test data download Cod
regression.
The root number can also be selected based on the actual situation.Regular Equation
In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $.
For example, four groups of property price forecasts:
Least Squares
$ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regular equations Gradient Descent:
Desired stride $ \ Alpha $;
Multiple iterations are requ
Original title:Given Arrays a[] and b[], each containing n distinct 2D points in the plane, design a subquadratic algorithm to count The number of points that is contained both in array a[] and array b[].The goal of the topic is to calculate the number of duplicate point, very simple, the code is as follows1 ImportJava.awt.Point;2 Importjava.util.Arrays;3 ImportJava.util.HashSet;4 ImportJava.util.Set;5 6 ImportEdu.princeton.cs.algs4.StdRandom;7 8 Public classplanepoints {9 PrivatesetNewHash
]; - } - System.out.println (arrays.tostring (aux)); the intL = 0; - intR =N; - for(intk = 0; k){ - if(l >= N) Break;//The array of auxiliary elements is exhausted, and the right side of the array does not need to be shifted. + Else if(R>=2*n) array[k]=aux[l++];//all elements of the right element of array are placed in the appropriate position, then simply move the elements of the auxiliary array to the right of the array - Els
Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no unanimous answer to the definition of machine learning.Arthur Samuel (1959) gives a definition of machine learning:Machine learning is about giving computers the ability to learn without explicit programming.Samuel designed a c
Support Vector MachinesI have the some issues to state. First, there were some bugs in original code which is caused by versions. I don ' t know ...There is three pictures u need to draw a division boundary. The first calls ' VISUALIZEBOUNDARYLINEAR.M ' which is fine and the others which call ' visualizeboundary.m ' can notDraw boundaries. So I check out this file and change the code ' contour (X1, X2, Vals, [0 0], ' Color ', ' B '); ' to ' Contour (X1, X2, Vals, [0.1 0.1], ' LineColor ', ' B ')
This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural network, the back is not necessarily seen.Of course, look at the process is still to do the notes to do homework, or read it is also a curs
Multivariate regressionReview simple linear regression: A feature, two correlation coefficients The actual application is much more complicated than this, such as1, house prices and housing area is not just a simple linear relationship.2, there are many factors affecting the price, not only the size of the house, but also many other factors. Now, in the first case, the price and the housing area are not simply linear, and may be two or polynomial:Two times function: Polynomial functions: P
?
This is determined by the characteristic value of the feature. There are two kinds of discrete value and continuous value, the distribution of discrete values is Poisson distribution, Bernoulli distribution, the distribution of continuous values is uniform distribution, normal distribution, chi-square distribution and so on. The reason why we assume the two eigenvalues of the above example is normal distribution is because the distribution of the majority of continuous-value variables
Questions -31 point Possible (graded)
Total time limit:
1000ms
Memory Limit:
65536kB
Describe
Write a two-dimensional array class Array2, so that the following program output is:
0,1,2,3,
4,5,6,7,
8,9,10,11,
Next
0,1,2,3,
4,5,6,7,
8,9,10,11,
Program:
#include
Add your code here
int main () { Array2 a (3,4); int i,j; fo
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