machine learning buzzwords

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

into the background do not occupy your currentin Redhat6.5When IP is configured , there is no result after network restart or no restartCd/etc/udev/rules.dDelete 70-persistent.rules 70-persistent-net.rulesRetry againto login Mysql-uroot-pwestos with a passwordGrant Select on test.* to [email protected] ' 172.25.49.4 ' identified by ' Westos ' ; Authorized Rpm-q Service Query rpm-e Service DeleteScheduled Tasks can be seen in/var/spool/cronThis article is from the "11889001" blog, please be su

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

This blog records "Machine Learning Combat" (machinelearninginaction) learning process, including algorithmic introduction and Python implementation. SVM (Support vector machine) SVM is a classification algorithm, through the analysis of training set data to find the best separation plane, and then use the flat face to

Support vector machine algorithm in deep learning does not fire up 2012 years ago, in machine learning algorithm is a dominant position, the idea is in the two classification or multi-classification tasks, the category of the super-plane can be divided into many kinds, then which kind of classification effect is the be

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

say we have some data points, and now we use a straight line to fit these points, so that this line represents the distribution of data points as much as possible, and this fitting process is called regression.In machine learning tasks, the training of classifiers is the process of finding the best fit curve, so the optimization algorithm will be used next. Before implementing the algorithm, summarize some

Machine learning Notes (i)Today formally began the study of machine learning, in order to motivate themselves to learn, but also to share ideas, decided to send their own experience of learning to the Internet to let everyone share.Bayesian learningLet's start with an exampl

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature learning tasks, such as machine vision, voice.In the following digita

Learning Guide for machine learning beginners (experience sharing)2013-09-21 14:47I computer research two, the professional direction of natural language processing, individuals interested in machine learning, so began to learn. So, this guy is a rookie ... It is because of