machine learning apis by example

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52 Useful machine learning and prediction APIs (various directional resources)

Author: Thuy T. Pham Selected from the Heart of Kdnuggets Machine compilation participation: Wu Yu Artificial intelligence is becoming the basic technology for a new generation of technology change, but developing artificial intelligence programs for their applications and businesses from scratch is expensive and often difficult to achieve the performance they want, but fortunately we have a large number of Ready-to-use

Built an online machine learning Webshell to detect restful APIs

AddressHttp:// Simple machine learning-based detection Webshell: Currently only supports PHP detection #使用方法: 1. Upload detection file interface address:/put Request by: POST Receive parameters: File For example: The current upload mode supports 2 format [php,zip] curl http:/ / [emailprotected] cu

Stanford University public Class machine learning: Neural Networks learning-autonomous Driving example (automatic driving example via neural network)

is going when it is initialized, or we don't know where the driving direction is, only after the learning algorithm has been running long enough that the white section appears in the entire gray area, showing a specific direction of travel. This means that the neural network algorithm at this time has chosen a clear direction of travel, not like the beginning of the output of a faint light gray area, but the output of a white section.Stanford Univers

Machine learning: The principle of genetic algorithm and its example analysis

In peacetime research, hope every night idle down when, all learn a machine learning algorithm, today see a few good genetic algorithm articles, summed up here.1 Neural network Fundamentals Figure 1. Artificial neural element modelThe X1~XN is an input signal from other neurons, wij represents the connection weights from neuron j to neuron I,θ represents a threshold (threshold), or is called bias (bias).

KNN (K Nearest Neighbor) for Machine Learning Based on scikit-learn package-complete example, scikit-learnknn

KNN (K Nearest Neighbor) for Machine Learning Based on scikit-learn package-complete example, scikit-learnknn KNN (K Nearest Neighbor) for Machine Learning Based on scikit-learn package) Scikit-learn (sklearn) is currently the most popular and powerful Python library for

How to Use machine learning to solve practical problems-using the keyword relevance model as an Example

Based on the literal Relevance Model of Baidu keyword search recommendation tool, this article introduces the specific design and implementation of a machine learning task. Including target setting, training data preparation, feature selection and filtering, and model training and optimization. This model can be extended to Semantic Relevance models, and the design and implementation of Search Engine releva

PHP Machine Learning Library PHP-ML Example Tutorial

PHP-ML is a machine learning library written using PHP. While we know that Python or C + + provides more machine learning libraries, in fact, most of them are slightly more complex and configured to be desperate for many novices. PHP-ML This machine

An example shows what machine learning is doing

We all should have the experience of buying watermelon in our lives. When buying watermelon, elders will give us experience, such as tapping on the surface of the melon to make some kind of sound is a good melon. The reason why elders will make good melons based on such characteristics is based on their life experience, and with the rich experience, they predict the ability of good melon is also improving. Herbert A. Simon has given the following definition of "

CUDA8.0 Matrix Multiplication Example Explanation (matrixMul.cpp) __ machine learning and GPU

Learn the use of Cuda libraries by learning the examples of Nvidia Matrixmul. Brief part of the rubbish. Just say the core code. This example is a matrix multiplication that implements C=a*b Use a larger blocks size for Fermi and above int block_size =; Original: dim3 Dimsa (5*2*block_size, 5*2*block_size, 1); Dim3 DIMSB (5*4*block_size, 5*2*block_size, 1); Reduce sizes to avoid ru

The EM algorithm in machine learning and the R language Example (1)

guesses, and certainly not very accurate at first. But based on this speculation, it can be calculated that each person is more likely to be male or female distribution. For example, a person's height is 1.75 meters, obviously it is more likely to belong to the male height of this distribution. Accordingly, we have a attribution for each piece of data. Then, according to the maximum likelihood method, the parameters of male height normal distribution

"Machine learning" K-Nearest neighbor algorithm and algorithm example

In machine learning, the classification algorithm is often used, and in many classification algorithms there is an algorithm named K-nearest neighbor, also known as KNN algorithm.First, the KNN algorithm working principleSecond, the application of the situationThird, the algorithm example and explanation---1. Collect data---2. Preparing the data---3. Design algor

Can machine learning really work? (2) (take the two-dimensional PLA algorithm as an example)

remaining B (n,k)? Take B (4,3) as an example to see if we can use B (3,?). Solve. B (4,3) = 11, can be divided into two categories: one is x4 in pairs appear, a class is x4 into a single appearance. Because k=3, so any 3 points can not shatter, namely: Α+β≤b (3,3). And because for 2α, X4 is in pairs appear, so, x1,x2,x3 any two points must not shatter, otherwise, plus X4, there will be three points are shatter. namely: Α≤b

Machine learning Notes (10) EM algorithm and practice (with mixed Gaussian model (GMM) as an example to the second complete EM)

[y_hat1==0]=3y_hat1[y_hat1==1]=0y_hat1[y_hat1==3]=1mu1=np.array ([Np.mean (X[Y_HAT1 = = i], axis=0) For I in range (3)]) print ' k-means mean = \ n ', Mu1print ' classification correct rate is ', Np.mean (y_hat1==y) gmm=gaussianmixture (n_components=3, Covariance_type= ' full ', random_state=0) (x) print ' gmm mean = \ n ', gmm.means_y_hat2=gmm.predict (x) y_hat2[y_hat2== 1]=3y_hat2[y_hat2==2]=1y_hat2[y_hat2==3]=2print ' classification correct rate for ', Np.mean (y_hat2==y)The output re

[Machine learning]KNN algorithm Python Implementation (example: digital recognition)

[i]) if (classifierresu Lt! = Datinglabels[i]): ErrOrcount + = 1.0 print "The total error rate is:%f"% (Errorcount/float (numtestvecs)) Print error count def img2vector (filename): Returnvect = zeros ((1,1024)) FR = open ( FileName) For I in range (+): LINESTR = Fr.readline () F or J in range (+): RETURNVECT[0,32*I+J] = Int (linestr[j]) RETURN RET Urnvectdef handwritingclasstest (): hwlabels = [] trainingfilelist = Listdir (' trainingDigits ') #load the training

Machine Learning: this paper uses the analysis of the taste of red wine as an example to describe the cross-validation arbitrage model.

Machine Learning: this paper uses the analysis of the taste of red wine as an example to describe the cross-validation arbitrage model. The least squares (OLS) algorithm is commonly used in linear regression. Its core idea is to find the best function matching of data by minimizing the sum of squares of errors. However, the most common problem with OLS is that it

Using whether to buy a house as an example to illustrate the use of decision tree algorithm-ai machine learning

purchases, and 12 for the total number of units. According to the formula of information entropy we can conclude that the information entropy of this data set is:Divided by lot (denoted by A1), Tri-Ring (D1), five-ring (D2), six-ring (D3), to calculate information gainBy whether near the Metro (denoted by A2), is (D1), no (D2), to calculate the information gaindivided by area (denoted by A3), 60 ping (D1), 80 ping (D2), to calculate information gainDivided by unit Price (expressed in A4), 5w (D

Python numpy machine Learning Library Use example

Installation sudo yum install NumPy From numpy Import * Produces an array Random.rand (4,5) Result Array ([[0.79056842, 0.31659893, 0.34054779, 0.97328131, 0.32648329], [0.51585845, 0.70683055, 0.31476985, 0.07952725, 0.80907845], [0.81623517, 0.61038487, 0.66679161, 0.77412742, 0.03394483], [0.41758993, 0.54425978, 0.65350633, 0.90397197, 0.72706079]]) Produce a matrix >>> Randmat=mat (Random.rand (bis)) >>> randmat.i Matrix ([[[1.72265179, 0.82071484, 0.8218207,-3.20005387], [0.60602642,-1.28

Classification of machine learning algorithms based on "machine Learning Basics"--on how to choose machine learning algorithms and applicable solutions

classification method.According to the different output space as the classification Second class classification (binary classification), commonly known as non-problem (say yes/no). Its output Space y={-1,+1} Multi-category Classification (Multiclass classification), Output space Y={1,2,...,k} Regression problem (regression), output space y=r, that is, the real range, the output is an infinite number of possible Structural Learn

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; probe into depth learning) __ Machine learning

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example appl

Stanford Machine Learning---The seventh lecture. Machine Learning System Design _ machine learning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

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