machine learning code example

VCL source code learning, official version source code example, vcl source code A classmate told me a few days ago that the third-party player used for video in their company is VCL, because I have always wanted to do this, and then I downloaded the compiled source

Amazon open machine learning system source code: Challenges Google TensorFlowAmazon took a bigger step in the open-source technology field and announced the opening of the company's machine learning software DSSTNE source code. Th

Absrtact: Recently in the "Machine learning actual Combat", in the process of code will always report some small errors, so the place of the debug; because it is jumping to see, so just a part of it, I hope that after the book I met all the errors are here to correct.Content:Nineth Chapter (regression tree): Mat0 = Dataset[nonzero (dataset[:,feature] >va

): # Extend the Input feature vector as a feature matrix linenum = featurematrix.shape[0] featurematrixin = Np.tile ( Featurevectorin, (linenum,1)) # Calculate the Euclidean distance between the matrix Diffmatrix = featurematrixin -Featurematrix Sqdiffmatrix = Diffmatrix * * 2 Distancevaluearray = Sqdiffmatrix.sum (Axis=1) Distancevaluearray = Distancevaluearray * * 0.5 return DistancevaluearrayUsed in the numpy of the more distinctive things. The practice is to first

Perceptron is an ancient statistical learning method, which is mainly applied to two types of linear data, and the strategy is to correct the error points on a given super-plane so that all points are correctly divided.The method used is the stochastic gradient descent method, which is linear and can guarantee the final convergence in finite step. Specific reference to Hangyuan Li's "Statistical learning me

"Machine Learning Combat" (HD Chinese version pdf+ HD English pdf+ source code)HD Chinese and HD English comparison learning, with directory bookmarks, can be copied and pasted;The details are explained and the source code is provided.Download: https://pan.baidu.com/s/1s77wm

, or K nearest neighbor (Knn,k-nearestneighbor) classification algorithm, is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.The core idea of the KNN algorithm is that if the majority of the k nearest samples in a feature space belong to a category, the sample also falls into this category and has the characteristics of the sample on

First, Logistic regression In the linear regression of machine learning, we can use the gradient descent method to get a mapping function hθ (x) H_\theta (x) to come and go close to the sample point, this function is a prediction of the continuous value. While logistic regression is an algorithm to solve the classification problem, we can get a mapping function F:x→y f:x→y by this algorithm, where x x is

Percent Machine learning Online class-exercise 4 neural Network learning% instructions%------------% This file contains Co De that helps you get started on the% linear exercise. You'll need to complete the following functions% of this exericse:%% sigmoidgradient.m% randinitializeweights.m% nncost function.m%% for the exercise, you'll not need to the change any

Download: https://pan.baidu.com/s/1Oeho172yfw1J6mCiXozQigTensorflow Machine Learning Practice Guide (Chinese Version pdf + English version PDF + Source Code)High-Definition Chinese PDF, 292 pages, with bookmarks, text can be copied and pasted;High Definition English PDF, 330 pages, with bookmarks, text can be copied and pasted;The Chinese and English versions can

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