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[10] Knowing: The use of "regularization to prevent fit" in machine learning is a principle [11] multivariable linear regression Linear regression with multiple variable [of] CS229 lecture notes [Equivalence of regression and maximum entropy models [i] Linear SVM and LR have any similarities and differences. Under what conditions the SVM and logistic regression are used respectively. [] Support Vector Mach

, as long as it is measured by error, residual vector (-1, 1,-1, 1) is its global optimal direction, this is gradient.Note: Figure 1 and Figure 2 have the same final effect, why do you need GBDT? The answer is to cross-fit. Over-fitting refers to the fact that in order to make the training set more accurate, there are many "rules set up only on the training set", which makes the current law of changing a dataset inapplicable. As long as the leaf nodes of a tree are allowed enough, the training s

: Choose your tool to see this article and see what you can do with the differentMLtools. Important: Always build a custom loss function that fits perfectly with your solution goals. Use an algorithm/method for all problems Many people will complete their first tutorial and immediately start using the same algorithms that they can imagine for each use case. This is very familiar and they think it can work like any other algorithm. This is a false h

synonym of the collection to find, which is the traditional index cannot do.Do not know according to this description, and then look at Wu Teacher's article, is not the SVD more clear? :-DResources:1) A Tutorial on Principal Component analysis, Jonathon ShlensThis is my main reference for using SVD to do PCA.2) HTTP://WWW.AMS.ORG/SAMPLINGS/FEATURE-COLUMN/FCARC-SVDA good idea about SVD, a few of my first pictures were taken from here.3) http://www.puf

An introductory tutorial on machine learning with a higher degree of identity, by Andrew Ng of Stanford. NetEase public class with Chinese and English subtitles teaching video resources (http://open.163.com/special/opencourse/ machinelearning.html), handout stamp here: http://cs229.stanford.edu/materials.htmlThere are a variety of similar course

Linux getting started Tutorial: Virtual Machine experience Xen The Virtual Machine System I want to experience is Xen. In the virtual machine field, Xen has a high reputation and its name is often used in various articles. At the same time, Xen also has a very high level of difficulty. It is not that easy to understand

Original: http://blog.csdn.net/abcjennifer/article/details/7700772This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vecto

description, and then look at Wu Teacher's article, is not the SVD more clear? :-DResources: 1) A Tutorial on Principal Component analysis, Jonathon Shlens This is my main reference to use SVD to do PCA 2) http://www.ams.org/samplings/feature-column/fcarc-svd a good idea about SVD, a few of my first pictures are from here; 3) http://www.puffinwarellc.com/index.php/news-and-articles/ articles/30-singular-value-decomposition-tutorial.html An

This is the process of recording self-study, the current theoretical basis is: University advanced mathematics + linear algebra + probability theory. Programming Basics: C/c++,pythonIn watching machine learning combat this book, slowly involved. I believe that the people who have read the above courses can begin to learn machine

the Boost library inside the program_options, Math_c99, unit_test_framework and other components. In addition, LIBXML2 is used. Currently, the latest version of Mlpack is 1.0.1, for more details, you can access the Mlpack documentation page.Related information:Http://www.csdn.net/article/2014-12-22/2823259-mlpack Managed Address: Https://github.com/mlpack/mlpack Official website: http://www.mlpack.org/ Tutorial: http://www.mlpack.org

Scikit-learn.org an open source machine learning Python Tool Http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html Theano-phthon Deep Learning Algorithm Library Python scientific computing Library Http://sebug.net/paper/books/scipydoc/index.html Machine

Original address: http://blog.csdn.net/abcjennifer/article/details/7716281This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Suppo

This blog summarizes the individual in the learning process of some of the papers, code, materials and common resources and sites, in order to facilitate the recording of their own learning process, put it in the blog.Machine learning(1) Machine learning Video Library-caltec

Tags: des style blog HTTP Io OS ar use I. Introduction This document is based on Andrew Ng's machine learning course http://cs229.stanford.edu and Stanford unsupervised learning ufldl tutorial http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial. Regression Problems in Mac