examples of machine learning projects

Choosing a machine learning Classifier by Edwin Chen On Wed April 2011 How does you know the learning algorithm to choose for your classification problem? Of course, if you really care about accuracy, your best bet was to test out a couple different ones (making sure to try Dif Ferent parameters within algorithm as well), and select the best on

This content resource comes from Andrew Ng's Machine Learning course on Coursera, where he pays tribute to Andrew Ng. The "Logic regression" study notes for the sixth course of machine learning at Stanford University, this course consists of 7 main parts:1) Classification (category)2) Hypothesis representation (modelin

Objective Because of the problem of image Learning machine learning, choose TensorFlow, but seems to go directly from the example of imagenet, but found how to find the end (Python will not, machine learning also do not understand), but according to my past experience, in th

baskets" are used as examples, such as x and Y respectively, to buy two kinds of goods, then we have the following three key measures of their relevance:1. The confidence level of the association rule X->y (confidence), that is, how much the customer buying x will buy y at the same time:650) this.width=650; "Src=" http://img.blog.csdn.net/20150413171447334?watermark/2/text/ ahr0cdovl2jsb2cuy3nkbi5uzxqvd2luzghhd2tfzmx5/font/5a6l5l2t/fontsize/400/fill/

This book is available in English electronic version: Machinelearning with R-second Edition [Ebook].pdf(included source)Evaluation Book: entry-level good book, introduced a variety of machine learning methods, all with r related to the implementation of the package, the case is very detailed, theory and examples combined. DirectoryChapter I. Introduction TO

garbage collector and Missioncontrol service suite and other parts of the implementation, are the leading position; virtual machines such as Azul VMS and Bea Liquid VMS are "high-performance" virtual machines. Looking ahead to the future of Java technology:(1) ModularIn the future Java platform, it is possible to provide syntactic support for modularity(2) Mixed languageWhen a single Java development has been unable to hide the complexity of the current software requirements, more and more Java

COMMON Pitfalls in machine learningJanuary 6, DN 3 COMMENTS Over the past few years I has worked on numerous different machine learning problems. Along the the I have fallen foul of many sometimes subtle and sometimes is subtle pitfalls when building models. Falling into these pitfalls would often mean when you think you had a great model, actually in Real-life

MySpace qizmt is a mapreduce framework designed to run and develop distributed computing application projects running on Windows Server large-scale clusters. MySpace qizmt is an open-source framework initiated by MySpace to develop trustworthy, scalable, and super-Simple distributed application projects. Open Source Address: http://code.google.com/p/qizmt /. Infer. NET is an open-source framework that runs

directly in this directory, which automatically compiles the makefile compiled edit script for the same directory, so the so file has been tested again!!! Passed the!!! Atari INSTALLATION COMPLETE!!!! 测试:python //进入python,最好是PY3import gym //load gym库,这里不能有报错env = gym.make("SpaceInvaders-v0") //新建一个打飞机游戏环境(这里可能会报错如下!!!)env.reset() //初始化env.render() //渲染,此时会弹出dialog,里面有飞机!就算OK了!env.close() //关闭env环境,dialog不能被gui关闭,只能用本行命令关闭! 5.

the courseware, mainly because the data set here is too small and there are only five data points. C ++ lab Considering that the above regression is essentially a least square problem. If we solve the least squares Ax = B from the perspective of linear algebra, here we will use eigen to do the experiment, which corresponds to the above two examples of 1 yuan and multivariate linear regression respectively. Always 1. for the exam area, if

Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of a supervised

The first article in the blog park, but will not be the last article. Although the name of machine learning sounds like a bluff, we know that every seemingly professional noun is used to make a small white one. So for those seemingly professional nouns, we need to understand what they are talking about, perhaps this is what I have been pursuing the spirit of hacker.The K-Nearest neighbor algorithm is a rela

1. Howding InequalitiesIn a jar, there are a lot of small balls, they are divided into two colors {orange, green}. Randomly grab n balls from a jar. Set: The percentage of orange balls in the jar is μ (unknown), and the ratio of the orange balls in the sample is ν (known). According to the howding inequality in probability theory (hoeffding's inequality) if n is large enough, ν is likely to be close to μ.Similarly, in machine

mistakenly classified data (x, y), there is-y (wx + B)> 0 (Buddha said: Too lazy to say ). Then there is a loss function (proving something to die ): 　　 Then the loss function is minimized (-_-zzz ): 　　 The perception machine learning algorithm is drive by mistake (the word "driven" sounds very powerful), and the Stochastic Gradient Descent Method (which will be written later ), evaluate the skewness for W

, and a system became moreis orderly, the information entropy is lower, conversely, the more chaotic a system, the higher the information entropy. So information entropy can be thought of as an orderly system.A measure of the degree of\[h (x) =-\sum_{i=1}^{n} p_{i} log_{2} p_{i} \]Third, information gain information GainThe information gain is for one characteristic, that is, to see a characteristic, the system has it and the amount of information when it is not, bothThe difference is the amount

. Naive Bayesian classifier has two kinds of polynomial model and Bernoulli model when it is used in text classification, and the algorithm realizes these two models and is used for spam detection respectively, which has remarkable performance.Note: Personally, the "machine learning Combat" naive Bayesian chapter on the text classification algorithm is wrong, whether it is its Bernoulli model ("word set") o