machine learning techniques and algorithms

, activating the back of the nerve layer, the final output layer of the nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get

classification method is used to solve the nonlinear problem in two steps, first using a transform to map the data of the original space to the new space, and then using the line-line classification learning method in the new space.Learn the classification model from the training data.If a kernel function is semi-positive, it is valid.In order to solve the problem of outliers, penalties are introduced. The new model should not only make the interval

machine Learning Algorithms Summary 1. Preface by using the machine learning algorithm to summarize the work, convenient for later search, rapid application. 2. Recommended Algorithms Cross Minimum Variance

conjugate gradient method is not only one of the most useful methods to solve the large scale linear equations,is also one of the most effective algorithms for solving large-scale nonlinear optimization. In various optimization algorithms, the conjugate gradient method is very important. Its advantage is that the required storage capacity is small, has step convergence, high stability, and does not require

introductionThe basic SVM classifier solves the problem of the 2 classification, the case of N classification has many ways, here is introduced 1vs (n–1) and 1v1. More SVM Multi-classification application introduction, reference ' SVM Multi-Class classification method 'In the previous method we need to train n classifiers, and the first classifier is to determine whether the new data belongs to the classification I or to its complement (except for the N-1 classification of i). The latter way we

of the total number of features with non-0 weights)9. Logistic regression : Two-dollar category, extremely efficient Giallo Computer System (many problems need to use probability estimates as output) two ways: "As is" "converted to two-dollar category" Application: Automatic diagnosis of disease (to investigate the risk factors that cause disease, and to predict the probability of disease occurrence according to risk factors), economic forecasts and other fieldsCategory: Evaluation indicators:

This article is about how to better apply machine learning algorithms in practice, such as the following empirical risk minimization issues:When solving the optimal, it is found that his error is very large, then how to deal with the current loss function value as small as possible? Here are a few options, and here's how to choose the right way to help with these

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on

Analysis of malware through machine learning: Basic Principles of clustering algorithms in Deepviz Since last year, we have discovered that many audiovisual companies have begun to engage in machine learning and artificial intelligence, hoping to find a fast and effective wa

Original: http://dataunion.org/13451.htmlXbinworld Introduction:In the field of machine learning, the so-called dimensionality reduction refers to the mapping of data points in the original high-dimensional space to the low-dimensional space. The essence of dimensionality is to learn a mapping function f:x->y, where x is the expression of the original data point, which is currently used at most in vector re

KNN algorithm of ten Algorithms for machine learningThe previous period of time has been engaged in tkinter, machine learning wasted a while. Now want to re-write one, found a lot of problems, but eventually solved. We hope to make progress together with you.Gossip less, get to the point.KNN algorithm, also called near

can be processed.Cons: Easy to fit.How to avoid overfitting:(1) dimensionality reduction, can use PCA algorithm to reduce the dimension of the sample, so that the number of theta of the model is reduced, the number of times will be reduced, to avoid overfitting;(2) regularization, the design of regular items regularization term.The regularization function is to prevent some properties before the coefficient weight is too large, there has been a fitting.Note that the way to resolve overfitting i

classes more equal. but .....Recall, though,that better data often beats better algorithms, and designing good features goes a long. And if you had a huge dataset, your choice of classification algorithm might not really matter so much in terms of Classi Fication performance (so choose your algorithm based on speed or ease of use instead).And if you really-accuracy, you should definitely try a bunch of different classifiers and select the best one by

specific job requirements, image algorithm For example, now deep learning hot not I said, so the basic convolution neural network algorithm , image classification , image detection The more famous paper in recent years should read it. If you have a condition, use it like a caffe,tensorflow frame.2. Machine Learning EngineerThis post is basically the same as the

Perception Machine: This is the simplest machine learning algorithm, but there are a few points to note. The first is the selection of the loss function, and in order to minimize the loss function, the gradient descent method used in the iterative process, finally obtains the optimal w,bThe visual interpretation is to adjust the value of the w,b, so that the sepa

. Or after the derivation of the formula can not be interpreted, or the number of unknown parameters is greater than the number of equations. At this point, the iterative algorithm is used to find the optimal solution step-after-step. In particular, if the optimization function is a convex function, then there is a global optimal solution, if the function is non-convex, then there will be many local optimal solutions, so the importance of convex optimization is self-evident. People always wan

]) *double (Dy[i])#Sqx = double (Dx[i]) **2Sumxy= VDOT (Dx,dy)#returns the point multiplication of two vectors multiplySQX = SUM (Power (dx,2))#Square of the vector: (x-meanx) ^2#calculate slope and interceptA = sumxy/SQXB= meany-a*MeanxPrintA, b#Draw a graphicPlotscatter (XMAT,YMAT,A,B,PLT)7.1.4 Normal Equation Group methodCode implementation of 7.1.5 normal equation set#data Matrix, category labelsXarr,yarr = Loaddataset ("Regdataset.txt")#Importing Data Filesm= Len (Xarr)#generate x-coordinat

value of 3.For example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].(4). Random_integers (low[, high, size]), similar to the above randint, the difference between the range of values is closed interval [low, high].(5). Random_sample ([size]), returns the random floating-point number in the half-open interval [0.0, 1.0]. If it is another interval [a, b), it can be converted (b-a) * Random_sample ([size]) + AFor example: (5-2) *np.random.ran

Nine algorithms for machine learning---naive Bayesian classifierTo understand the Naive Bayes classificationBayesian classification is a generic term for a class of classification algorithms, which are based on Bayesian theorem, so collectively referred to as Bayesian classification. Naive naive Bayesian classification

hope for in the earthquake prediction is that the recall is very high, that is to say, every earthquake we want to predict. We can sacrifice precision at this time. 1000 alarms are preferred, 10 earthquakes are predicted correctly, and do not predict 100 times 8 leaks two times. Suspects convictedBased on the principle of not blaming a good man, we hope to be very accurate about the conviction of a suspect. In time, some criminals were spared (recall low), but also worthwhile. Regressi