machine learning sample code

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

the above figure, we can see that the output of neurons is: 2. Learning rules for Perceptron: As I said before, the Perceptron has the ability to learn and adapt, so how does he learn, we look at the picture below Here, let's explain his process: First, we enter the training sample X and the initialization weight vector W, the vector point multiplication, then the point multiplication result is used to ac

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

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

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

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

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

Python code implementation on the perception machine ----- Statistical Learning Method Reference: http://shpshao.blog.51cto.com/1931202/1119113 1 #! /Usr/bin/ENV Python 2 #-*-coding: UTF-8-*-3 #4 # Untitled. PY 5 #6 # copyright 2013 T-dofan There are still a few questions, the book's adjustment strategy is: Wi = wi + Nyi * Xi, so it is necessary to multiply t

-0.576525 11.778922 0-0.346811-1.678730 1-2.124484 2.672471 11.217916 9.597015 0-0.733928 9.098687 0-3.642001-1.618087 10.315985 3.523953 11.416614 9.619232 0-0.386323 3.989286 10.556921 8.294984 11.224863 11.587360 0-1.347803-2.406051 11.196604 4.951851 10.275221 9.543647 00.470575 9.332488 0-1.889567 9.542662 0-1.527893 12.150579 0-1.185247 11.309318 0-0.445678 3.297303 11.042222 6.105155 1-0.618787 10.320986 01.152083 0.548467 10.828534 2.676045 1-1.237728 10.549033 0-0.683565-2.166125 10.229

)]=1 else:print "The word:%s is not in my vocabulary!" %word return returnvecdef TRAINNBC (trainsamples,traincategory): Numtrainsamp=len (Trainsamples) NumWords=len (train Samples[0]) pabusive=sum (traincategory)/float (numtrainsamp) #y =1 or 0 feature Count P0num=np.ones (numwords) P1NUM=NP.O NES (numwords) #y =1 or 0 category count P0numtotal=numwords p1numtotal=numwords for I in Range (Numtrainsamp): if Traincategory[i]==1:p0num+=trainsamples[i] P0numtotal+=sum (Trainsamples[i]) E

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

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

number D is too large, λ too low, sample size is too small. This provides the basis for us to improve the machine learning algorithm. ============================== Second lecture ============================== Design ====== of ======= machine learning system (i) The des

: %f" % (errorCount/float(mTest))handwritingClassTest() One result (k = 3): k = 7The correct rate is not equalk = 3Better Time: In the process of Handwritten Digit Recognition, the accuracy decreases as the K value increases. The value of k is not larger, the better. So far, k-Nearest Neighbor Algorithm learning and instance verification have been completed. Compared with other machine

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

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

theory, according to the difference between data sampling distribution and real distribution, the learning mechanism of probability approximation approximation (PAC) is formed, and the traditional statistical learning theory is developed on this basis. In order to avoid the ill-posed problem of objective function in data prediction, a series of regularization theories are proposed, such as the sparse

Sample Code for adding a verification code to spring security4 and sample code for security4 Spring security is a large module. This article only covers the authentication of custom parameters. The default verification parameters of spring security are username and password,