C + + Programming tutorialC + + programming ideasC + + University tutorialsC + + programming languageData structure algorithm and application C + + language descriptionC + + Standard Template Library------Self-study tutorials and reference manualsGeneric Programming and STLDeep Exploration of the C + + object modelDesign pattern---The basis of reusable object-ori
WEEK1:Machine learning:
A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E.
Supervised learning:we already know what we correct output should look like.
Regression:try to map input variables to some continuous function.
clusters. Clustering is when you don't know exactly how many classes the target database has, and you want to make all the records into different classes or clusters, and in this case, The similarity of a metric (for example, distance) is minimized between the same cluster and maximized among different clustering classes. Unlike classification, unsupervised learning does not rely on a predefined class or band-mark training instance, which needs to be
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Mathematics is the foundation of computer technology, linear algebra is the basis of machine learning and deep learning, the best way to understand the knowledge of the data I think is to understand the concept, mathematics is not only used for exams in school, but also the essential basic knowledge of the work, in fact, there are many interestin
This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'E:/python
also the best book I personally think to learn about LINQ.
Chapter 2 is a summative chapter of this book. In general, this book is a very good learning book with C # language features. It is definitely worth reading and has said
is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process i
Machine learning practices in python3.x and python machine learning practices
Machine Learning Practice this book is written in the python2.x environment, while many functions and 2 in
Directory
1. Introduction
1.1. Overview
1.2 Brief History of machine learning
1.3 Machine learning to change the world: a GPU-based machine learning example
1.3.1 Vision recognition based on depth neural network
1.3.2 Alphago
1.3.
Machine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) BeginnerMachine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) Beginner[Email protected]Http://blog.csdn.net/zouxy09Machine lear
numeric Type- int, float, long, complex There are four different numeric types: ordinary integers, long integers, floating-point numbers, and complex numbers A normal integer (or short integer) is implemented using a long in C with a precision of at least 32 bits (Sys.maxint is always set to the maximum normal integer value of the current platform, and the minimum value is -sys.maxint - 1). long integers have infinite precision. floating-point numbe
that if the sampled data is biased, then the effect of learning is also biased, this situation is called sampling deviation.In reality, we need training data and test data from the same distribution.To avoid this problem, what we can do is to understand the test environment, so that the training environment or training data and test environment as close as possible.Data snooping (snooping)Any process you use data is indirectly prying into the data, s
in mat: for j in range(0,m): if i[j]>MaxNum[j]: MaxNum[j]=i[j] for p in mat: for q in range(0,m): if p[q]
Library implementation: Input matrix mat,
GetAverage (mat): returns the mean value.
GetVar (average, mat): returns the variance
DenoisMat (mat): de-noise
AutoNorm (mat): normalization Matrix
: Https://github.com/jimenbian/AutoNorm-mat-
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* This article is from the blog "Li bogarvin"
* Reprin
This article is translated from awesome-machine-learning by bole online-toolate. Welcome to the technical translation team. For more information, see the requirements at the end of the article.
This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning fi
This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning field ).C ++ Computer Vision
CCV-Machine Vision Library Based on C Language/provided Cache/core, novel machine
Machine Learning 4, machine learning
Probability-based classification method: Naive BayesBayesian decision theory
Naive Bayes is a part of Bayesian decision-making theory. Therefore, before explaining Naive Bayes, let's take a quick look at Bayesian decision-making theory knowledge.
The core idea of Bayesian decision-m
Learn the C + + book listTurn fromHttp://stackoverflow.com/questions/388242/the-definitive-c-book-guide-and-listBeginnerintroductoryIf you were new to programming or if you had experience in other languages and is new to C + +, these books is highly reco Mmended.
Part I: ClassificationThe first two parts of the book focus on supervised Learning (supervisedieaming). In the process of supervising learning, we only need to give the input sample set , and the machine can push the possible results of the specified target variable from it. Supervised
not equal to i while (j==i): j = int(random.uniform(0,m)) return j def clipAlpha(aj,H,L): if aj > H: aj = H if L > aj: aj = L return aj def smoSimple(dataMatIn, classLabels, C, toler, maxIter): dataMatrix = mat(dataMatIn); labelMat = mat(classLabels).transpose() b = 0; m,n = shape(dataMatrix) alphas = mat(zeros((m,1))) iter = 0 while (iter
The running result is shown in figure 8:
(Figure 8)
If you are interested in the above code,
language makes the boring knowledge of C ++ lively. Learning is no longer a boring thing, but an interesting tour of the C ++ world. If you are a C ++ Medium-and High-level user who has some knowledge of C ++ and is seeking to upgrade to the
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