coursera ml

defined in LIBC.lib (tolower.obj)LIBCMT.lib (tolower.obj): Error LNK2005: _tolower already defined in LIBC.lib (tolower.obj)Wait a minute.So the initial estimate is a compiler problem, by searching online and viewing MSDN, the problem with the Visual C + + compiler option about single-threaded or multithreaded run-time routines: My static library compiled with/ML single-threaded version, and the program referencing it is/MT multithreaded version, the

VC Compilation Options - 多线程(/MT) - 多线程调试 (/MTd) - 多线程DLL (/MD) - 多线程调试DLL (/MDd)C run-Time library files - Single thread(static link) ML libc.lib - Debug single thread(static link) MLd libcd.lib - MultiThread(static link) MT libcmt.lib - Debug multiThread(static link) MTd libcmtd.lib - MultiThread(dynamic link) MD msvert.lib - Debug multiThread(dynamic link

)Libcmt. Lib (tolower. OBJ): Error lnk2005: _ tolower already defined in libc. Lib (tolower. OBJ)Libcmt. Lib (tolower. OBJ): Error lnk2005: _ tolower already defined in libc. Lib (tolower. OBJ)And so on. The preliminary estimation is about the compiler. By searching and viewing msdn on the internet, it turns out to be about the Single-thread or multi-thread runtime routines of Visual C ++ compiler options: my static library is in/ml single-threaded ve

equivalent to "Least Squares. 7.3.1 Maximum Likelihood Estimation derivation In the following derivation, note that the "format" of x and W is very simple. Note that when processing "vectors", the standards are column vectors. If a book appears in the form of a single vector, you can only say: Throw it. Detailed derivation: 7.3.2 ry After finding W, the ry of y_hat = w'x is that y_hat is the projection point of Y in the Space Formed by X columns ". 7.3.3 convex Convexity Only when the funct

Generally, classification (or regression) is divided into two types: Parameter Learning and instance-based learning. The form of parameter learning is to learn the parameters of the corresponding model through a pile of training data, and then the training data is useless. For new data, the learned parameters can be used to draw a conclusion; Instance-based learning (also called memory-Based Learning) also uses training data such as KNN algorithm during prediction. Instance-based learning gene

. libmultithread (static link) mtlibcmt. libdebug multithread (static link) mtdlibcmtd. libmultithread (Dynamic Link) mdmsvert. libdebug multithread (Dynamic Link) mddmsvertd. roles of LIB 2.c runtime libraries C Runtime libraries not only provide us with necessary library function calls (such as memcpy, printf, and malloc, it also provides the most important function to add a startup function for the application. The main function of the C Runtime Library startup function is to initialize the p

Youku Update Address: http://list.youku.com/albumlist/show?id=26979872ascending=1page=1The official use of Python's Scikit-learnA Hello WorldDecision tree for a simple fruit classifierFrom sklearn Import treefeatures = [[[1], [1], [0], [0]]labels], [0,0,1,1]CLF = tree. Decisiontreeclassifier () CLF = Clf.fit (features,labels) print clf.predict ([[150,0]])Two visualizing a decision TreeMany types of classifiers Artificial neural network Support Vector Machine .... Why Choose

02 |. K.. abbbbccc...|3083 0000c0a0 01 00 00 00 18 00 23 00 00 00 00 02 06 00 00 00 |......#.........|3084 0000c0b0 3c AD B2 db 01 10 61 65 65 65 65 66 | 3085 0000C0C0------------------A63086 0000c0d0 (3c B2 b5) in the Fe-|....3087 0000c0e0 (XX) at the xx xx, at xx xx xx xx, |f...............|3088 0000c0f0 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 |................|First row of data:03 02 02 01/* Variable-Length field */----table, 4 field types are varchar, and there is no null data, and

and through E (y) = 0.138 mobile y) To Get X = (−2.8, −1.8, −0.8, 1.2, 4.2) and Y = (−0.028, −0.018, −0.008, 0.012, 0.042), from (4) Pearson Constraints From the above explanation, we can also understand Pearson's constraints: 1. Wired relationship between two variables2. The variable is a continuous variable.3. All variables conform to the normal distribution, and the binary distribution also conforms to the normal distribution.4. Two variables are independent. In practice statistic

(iris$species,result1$cluster) 1 2 3 setosa 0 3 0 virginica 0Example of 3d effect display# install.packages ("Scatterplot3d") Library (Scatterplot3d) Scatterplot3d (result1$membership, Color=result1$cluster, type="H ", angle=55, scale.y=0.7, pch=16, main="pertinence")Resources: http://blog.csdn.net/Alex_luodazhi/article/details/47125149 Https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Fuzzy_Cluste

: Constructing a classification tree with the fastest descending entropy valueA decision tree is a tree structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. The top-down recursive method is used to select the feature with the greatest information gain as the current splitting feature.6 SVM: Constructing super plane, classifying nonlinear data7 K-means: Calculating centroid, clustering without tag

In China, Coursera is very choppy and often gets stuck when playing half of the video. I don't know why. Therefore, you can only download the file and view it again. There is a script on GitHub to open the link to download the entire course. It is very convenient to use. The method is as follows. Because this script uses multiple Python libraries, it is best to use the Linux system. I use Debian Wheezy and python2.7.3. Of course, you need a

Coursera-getting and Cleaning Data-week4Thursday, January,Make up the fourth week notes, and this course summary.The four-week course focuses on text processing. Inside includes1. Handling of variable names 2. Regular Expression 3. Date processing (see Swirl lubridate package exercise)First, the processing of variable names, followed by two principles, 1) uniform case tolower/toupper;2) Remove the import data, because special characters caused by the

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

} - 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

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