matrix factorization machine learning

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 algorithm Analysis data---4. Test algorithmFirst,

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

second largest corresponding eigenvector (the solution to the eigenvectors are orthogonal). Which λ is our variance, also corresponds to our previous maximum variance theory, that is, to find a projection can make the most difference between the line.Matlab implementation function [Lowdata,reconmat] = PCA(data,k) [Row, col]=size(data); Meanvalue = mean (data);%vardata = var (data,1,1);Normdata = data-Repmat(Meanvalue,[Row,1]); Covmat = CoV (Normdata (:,1), Normdata (:,2));The covariance

Many friends want to learn machine learning, but suffer from the construction of the environment, here is the Windows Scikit-learn Research and development environment to build steps.Step 1. Installation of PythonPython has versions of 2.x and 3.x, but many good machine learning Python libraries do not support 3.x, so

is unroll into a vector, then using the existing gradient descent algorithm in the library to find the optimal parameters, and finally reshape into a matrix form; The reason for this is that the parameters of the ready-made gradient descent algorithm, the Inittheta requirement, must be in the form of a vector.3,gradient CheckingThis is a mathematical method to seek partial derivative.It can be used to verify that the gradient descent algorithm is imp

Shanghai Jiao Tong University Zhang Zhihua teacher's public course "Introduction to Machine learning", Course Link: http://ocw.sjtu.edu.cn/G2S/OCW/cn/CourseDetails.htm?Id=397 for three days, take notes. OK, straight to the subject.(i) Basic Conceptsdata Mining and machine learning essence is matter son, ML more close t

learning: If DVC (H) is finite, gε H will be generalized (theoretically proven in Lesson 6 ). Note: generalization in Machine Learning refers to the ability to apply the rules obtained by samples to data outside the samples, that is, the gap between EIN and eout. The preceding statement has the following attributes: 1. It has nothing to do with

Law in disguise by solving the objective function of the first-order zero parameter value, and then obtain the minimum value of the objective function. then the iterative formula is written:When θ is a vector, the Newton method can be expressed by the following formula:Where h is called the Haisen matrix is actually the second derivative of the objective function to the parameter θ.By comparing the iterative formulas of Newton method and gradient des

Summary:Mlpack is a scalable C + + machine learning library designed to allow new users to use machine learning with simple, consistent APIs while providing professional users with high performance and maximum flexibility in C + +.Mlpack is an intuitive, fast, and scalable C + + ma