machine learning mastery

This article is reproduced from: http://www.csdn.net/article/2015-10-01/2825840 Absrtact: Deep learning based on Hadoop is an innovative method of deep learning. The deep learning based on Hadoop can not only achieve the effect of the dedicated cluster, but also has a unique advantage in enhancing the Hadoop cluster, distributed depth

Machine learning Algorithms Study NotesGochesong@ Cedar CedroMicrosoft MVPThis series is the learning note for Andrew Ng at Stanford's machine learning course CS 229.Machine learning Al

To learn about machine learning, you must master a few mathematical knowledge. Otherwise, you will be confused (Allah was in this state before ). Among them, data distribution, maximum likelihood (and several methods for extreme values), deviation and variance trade-offs, as well as feature selection, model selection, and hybrid model are all particularly important. Here I will take you to review the releva

Python Machine Learning Theory and Practice (4) Logistic regression and python Learning Theory From this section, I started to go to "regular" machine learning. The reason is "regular" because it starts to establish a value function (cost function) and then optimizes the val

The fate of life, strange and difficult to test.I thought the time was devoted to Java, but did not want to break into the hall of machine learning. That summer, the scorching sun, across 1000 kilometers to the strange city of wandering, I hope all this is worthwhile.I Java origin, slightly understand c,linux, database, technology slag slag.Hope every step of life is a new starting point, each step has a ne

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine learning. This course

of a nonlinear function sigmoid, and the process of solving the parameters can be accomplished by the optimization algorithm. In the optimization algorithm, the gradient ascending algorithm is the most common one, and the gradient ascending algorithm can be simplified to the random gradient ascending algorithm.2.SVM (supported vector machines) Support vectors machine:Advantages : The generalization error rate is low, the calculation cost is small, the result is easy to explain.　　　　cons : Sensit

Finally the end of the final, look at others summary: http://blog.sina.com.cn/s/blog_641289eb0101dynu.htmlContact Machine Learning also has a few years, but still only a rookie, when the first contact English is not good, do not understand the class, what things are smattering. After learning some open classes and books on the go, I began to understand some conce

Reprinted from: Http://www.cnblogs.com/shishanyuan/p/4747761.html?utm_source=tuicool1. Machine Learning Concept1.1 Definition of machine learningHere are some definitions of machine learning on Wikipedia:L "Machine

two classification problem, so the model is modeled as Bernoulli distributionIn the case of a given Y, naive Bayes assumes that each word appears to be independent of each other, and that each word appears to be a two classification problem, that is, it is also modeled as a Bernoulli distribution.In the GDA model, it is assumed that we are still dealing with a two classification problem, and that the models are still modeled as Bernoulli distributions.In the case of a given y, the value of x is

Use Python to master machine learning in four steps and python to master machines in four steps To understand and apply machine learning technology, you need to learn Python or R. Both are programming languages similar to C, Java, and PHP. However, since Python and R are both relatively young and "Far Away" from the CP

Fortunately with the last two months of spare time to "statistical machine learning" a book a rough study, while combining the "pattern recognition", "Data mining concepts and technology" knowledge point, the machine learning of some knowledge structure to comb and summarize:Machine

July algorithm December machine learning online Class---20th lesson notes---deep learning--rnnJuly algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com Cyclic neural networks Before reviewing the knowledge points:Full

IntroductionIn real life, we may unknowingly use a variety of machine learning algorithms every day. For example, when you use Google every time, it works well, and one of the important reasons is that a learning algorithm implemented by Google can "learn" how to rank pages. Every time you use a Facebook or Apple photo-processing app, they can automatically ident

This article is from: http://blog.jobbole.com/56256/This is a hard-to-write article because I hope this article will inspire learners. I sat down in front of the blank page and asked myself a difficult question: what libraries, courses, papers, and books are best for beginners in machine learning.It really bothers me how to write and write nothing in the article. I have to think of myself as a programmer and a beginner of

1. Training error: The error of the learner in the training set, also known as "experience Error"2. Generalization error: The error of the learner on the new sampleObviously, our goal is to get a better learner on a new sample, which is a small generalization error.3. Overfitting: The learner learns the training sample too well, leading to a decline in generalization performance (learning too much ...). Let me think of some people bookworm, reading de

First thanks to the machine learning daily, the above summary is really good. This week's main content is the migration study "Transfer learning" Specific Learning content: Transfer Learning Survey and Tutorials"1" A Survey on Transfer