statistics and machine learning toolbox

It seems that mathematics is always not enough. These days, in order to solve some problems in research, we held a textbook on mathematics in the library. From the university to the present, the number of Mathematics Courses in the classroom and the number of self-taught mathematics courses is not very small. However, during the study, we always find that new mathematical knowledge needs to be supplemented. Learning and vision are the intersection of

Https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.mdMachine-learning/data Mining An Introduction to statistical learning-book + R Code Elements of statistical Learning-book Probabilistic Programming Bayesian Methods for Hackers-book + IPytho

1. Pay attention to the fields involved in the Method In essence, machine learning is a multidisciplinary field. It draws on the results of artificial intelligence, probability statistics, computational complexity theory, control theory, information theory, philosophy, biology, neurobiology, and other disciplines ."Thinking: When studying and understanding a sp

Objective:This series is in the author's study "Machine Learning System Design" ([Beauty] willirichert) process of thinking and practice, the book through Python from data processing, to feature engineering, to model selection, the machine learning problem solving process one by one presented. The source code and data

Reference booksDeep learningDeep learning is a new field in machine learning research, and its motive is to establish and simulate the neural network of human brain import analysis and learning, which imitates the mechanism of human brain to interpret the data.Examples of images, sounds and text. Deep

This paper mainly includes the realization of common machine learning algorithms, in which the mathematical derivation, principle and parallel implementation will give the link. Machine Learning (machines learning, ML) is a multidisciplinary interdisciplinary s

(refer to theCoursera public Lesson Note: Stanford University's seventh lesson on machine learning "regularization (regularization)").Note:θ0 is a constant, x0=1 is fixed, then θ0 does not need to punish the factor, the ridge regression formula I of the first element to be 0.This is done by introducing λ to limit the sum of squared errors by attracting the penalty. To reduce the number of unimportant param

Tags: RTC information percent Element data mining SSIS estimate DIA codestatistical methods in machine learning .Statistics is a pillar of machine learning.Primitive observations are just data, but they are not information or knowledge. Data raises problems, such as: What is the most common or expected observa

IntroductionI feel that learning machine learning algorithms is the only way to get started from a mathematical perspective, the machine learning field, the machine learning definition

(a) KNN is still a supervised learning algorithmThe KNN (K Nearest neighbors,k nearest neighbor) algorithm is the simplest and best understood theory in all machine learning algorithms. KNN is an instance-based learning that calculates the distance between new data and the characteristic values of the training data, an

paste directly for the new project. 1.2.1 CourseYou need to know how to use the Python ecosystem to accomplish every sub-task in machine learning. Once you know how to use this platform to complete any of them, and get a reliable result, you can repeat the process in future projects. Let's start with the general flow of a machine

Original: http://www.zhihu.com/question/27068705What are the differences and linkages between bias (deviations), error (Error), and variance (variance) in machine learning? Modification recently in Learning machine learning, learning