coursera algorithms

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Coursera algorithms week2 Basic sort interview Questions:1 intersection of the sets

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

Coursera Algorithms week3 Merge sort exercise quiz 1:merging with smaller auxiliary array

]; - } - 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

Coursera algorithms Week3 Quick Sort Exercise quiz: Selection in two sorted arrays (looking for the K-element from both ordered arrays)

} - to Public Static voidMain (string[] args) { + intn = 10; - intN1 =stdrandom.uniform (n); the intN2 = nN1; * int[] A =New int[N1]; $ int[] B =New int[N2];Panax Notoginseng for(inti=0;i){ -A[i] = stdrandom.uniform (100); the } + for(inti=0;i){ AB[i] = stdrandom.uniform (100); the } + Arrays.sort (a); - Arrays.sort (b); $System.out.println ("a=" +arrays.tostring (a)); $System.out.println ("b=" +arrays.tostr

Python detailed process of crawling Coursera course resources, coursera Course Resources

Python detailed process of crawling Coursera course resources, coursera Course Resources Sometimes we need to add some classic things to our favorites and review them from time to time. Some courses on Coursera are undoubtedly classic. Most of Coursera's finishing courses provide complete teaching resources, including ppt, video, and subtitles. It is very easy to

What courses are worth learning about Python and data analysis on coursera?

RT reply: I strongly recommend the python course at rice University. The course is well designed and the teacher is very responsible. ----------------------------------------------------------- Answer questions by phone last night. Update the questions today; There are a total of three courses at Rice University, which now seems to have been divided into six. Each course lasts for 8 weeks in a simple order. The first course is the basics of Python and introduces the basic syntax of Python; The

What are some of the learning Python, data analysis courses on Coursera?

Rt Reply content:I highly recommend the Python class at Rice University, which is very well designed and the teacher is very responsible. ----------------------------------------------------------- Last night mobile phone answer, updated today; Rice University has a total of 3 courses, now seemingly dismantled into 6 doors, 8 weeks per course, according to the order of the more-than-digest. The first course is the Python Foundation, which introduces the basic syntax of Python. The second cours

[Machine Learning] Coursera notes-Support Vector machines

PrefaceThe Machine learning section records Some of the notes I have learned in the process of learning, including the online course or tutorial's study notes, the reading notes of the papers, the debugging of algorithmic code, the thinking of cutting-edge theory and so on, which will open different column series for different content.Machine learning is an exciting and fascinating field of research, with both wonderful theoretical formulas and practical engineering techniques, and in the proces

Coursera course Download and archive plan [reprint]

In Wednesday, we received mass mailings from the Coursera platform, to the effect that Coursera will completely close the Old Course platform on June 30, upgrade to the new course platform, some Old Course resources (course videos, course materials) will not be saved, if you have previously studied the relevant courses, or have the desired courses , Coursera reco

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

. -Get more training samples -Try to use a set with fewer features -Try to obtain other features -Try to add multiple combinations of features -Try to reduce λ -Add Lambda Machine Learning (algorithm) diagnosis (Diagnostic) is a testing method that enables you to have a deep understanding of a Learning Algorithm and know what can be run and what cannot be run, it also guides you how to maximize the performance of learning algorithms. Although the diag

Operating system Learning notes----process/threading Model----Coursera Course notes

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

[Machine Learning] Coursera ml notes-Logistic regression (logistic Regression)

IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford Andrew Ms Ng's tutorials in Coursera and online courses such as UFLDL Tutorial,stanford cs231n and Tutorial, as well as a large number of online related materials (listed later). PrefaceThis article mainly int

Machine Learning| Andrew ng| Coursera Wunda Machine Learning Notes

continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Text detection Character segmentation Ch

Coursera Machine Learning Study notes (i)

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

Python crawls the detailed process of Coursera course resources

Sometimes we need to put some classic things in the collection, always aftertaste, and Coursera on some of the courses are undoubtedly classic. Most of the end courses in Coursera provide a complete set of teaching resources, including PPT, video and subtitles, which will be very easy to learn when offline. Obviously, we will not go to a file to download a file, Only fools do so, programmers are smart! Wha

Notes | Wunda Coursera Deep Learning Study notes

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of machine learning and AI products under various business scenarios. Wunda Coursera Deep Learni

Coursera Machine Learning Chapter 9th (UP) Anomaly Detection study notes

is still good. It is estimated that P (x) is the density estimation problem. Anomaly Detection Algorithm Description:1. Select the characteristic XI that is considered to reflect the exception example.2. Using training sets and formulas to fit parameters μ and σ, different characteristics correspond to different parameters and Gaussian distributions.3. For a new example X, using the Gaussian distribution formula, fitted parameters, and P (x) formulas to calculate P (x), p (x) To do a problem:D9

Detailed process of Python crawling Coursera course resources

This article mainly introduces the detailed process of Python crawling Coursera course resources. For more information, see some typical things, some Coursera courses are undoubtedly classic. Most of Coursera's finishing courses provide complete teaching resources, including ppt, video, and subtitles. it is very easy to learn offline. Obviously, we won't download a file or a file. it's just a fool. programm

Python crawls the detailed process of Coursera course Resources _python

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

[Coursera] Getting and cleaning Data Quiz

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

"MATLAB" machine learning (Coursera Courses Outline & Schedule)

The course covers technology:Gradient descent, linear regression, supervised/unsupervised learning, classification/logistic regression, regularization, neural network, gradient test/numerical calculation, model selection/diagnosis, learning curve, evaluation metric, SVM, K-means clustering, PCA, Map Reduce Data Parallelism, etc...The course covers applications:Message classification, tumor diagnosis, handwriting recognition, autonomous driving, model optimization, OCR, etc...

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