statistics and machine learning toolbox

", a book written by Chinese scientists, is quite understandable.6. "Managing gigabytes", a good book of information retrieval.7. "Information theory:inference and Learning Algorithms", reference books, relatively deep.Relevant mathematical basis (reference books, not suitable to read through):1. Linear algebra: This reference book is not listed, many.2. Matrix Mathematics:"Matrix Analysis", Roger Horn. The undisputed classic of matrix analysis.3. Pro

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This 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 vector machines), clustering, dimensionality reduc

: http://blog.echen.me A post/month, a more practical topic Machine learning, http://www.machinedlearnings.com A post/month, more practical topic, usually around big data learning Flowingdata, http://flowingdata.com A thread/day, mainly to solve some statistical problems

Machine learning is a comprehensive and applied discipline that can be used to solve problems in various fields such as computer vision/biology/robotics and everyday languages, as a result of research on artificial intelligence, and machine learning is designed to enable computers to have the ability to learn as humans

Directory 1. Introduction 1.1. Overview 1.2 Brief History of machine learning 1.3 Machine learning to change the world: a GPU-based machine learning example 1.3.1 Vision recognition based on depth neural network 1.3.2 Alphago 1.3.

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up

July online April machine learning algorithm class notes--no.1 Objective Machine learning is a multidisciplinary interdisciplinary, including probability theory, statistics, convex analysis, feature engineering and so on. Recently followed the July algorithm to learn the kno

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/1kVNe5EJ 1. course Introduction 2. Machine

: http://blog.echen.me A post/month, a more practical topic Machine learning, http://www.machinedlearnings.com A post/month, more practical topic, usually around big data learning Flowingdata, http://flowingdata.com A thread/day, mainly to solve some statistical problems

-virginica 6.588 2.974 5.552 2.026Df.groupby (' class '). describe ()Data is split by class and descriptive statistics are given separatelyPetal length \Count mean std min 25% 50% 75% maxClassIris-setosa 50.0 1.464 0.173511 1.0 1.4 1.50 1.575 1.9Iris-versicolor 50.0 4.260 0.469911 3.0 4.0 4.35 4.600 5.1Iris-virginica 50.0 5.552 0.551895 4.5 5.1 5.55 5.875 6.9Petal width ... sepal length sepal width \Count mean ... 75% max Count meanClass ...Iris-setos

, it is also constrained, and the angle will have bounded range.So how do you optimize for these problems? A good way to do this is to assume that your problem can be reparameterization (re-parameterized), and after you reparameterize your model, the model constraint is gone. The influence of this thought is very far-reaching, in fact a lot of standard constrained problem, after reparameterize, becomes the problem without constraint.If you want to optimize a probability distribution,

In machine learning, often need to calculate the distance between each sample, used for classification, according to distance, different samples grouped into a class; But in the current machine learning algorithm, the distance calculation mode is endless, then this blog is mainly to comb the current

-virginica 6.588 2.974 5.552 2.026Df.groupby (' class '). Describe ()Data is split by class and descriptive statistics are given separatelyPetal length \Count mean std min 25% 50% 75% maxClassIris-setosa 50.0 1.464 0.173511 1.0 1.4 1.50 1.575 1.9Iris-versicolor 50.0 4.260 0.469911 3.0 4.0 4.35 4.600 5.1Iris-virginica 50.0 5.552 0.551895 4.5 5.1 5.55 5.875 6.9Petal width ... sepal length sepal width \Count mean ... 75% max Count meanClass ...Iris-setos

This is already the third algorithm of machine learning. Speaking of the simple Bayes, perhaps everyone is not very clear what. But if you have studied probability theory and mathematical statistics, you may have some idea of Bayesian theorem, but you can't remember where it is. Yes, so important a theorem, in probability theory and mathematical

language is the same, but the syntax and API are slightly different. R Project for statistical Computing: This is a development environment that employs a scripting language similar to Lisp. In this library, all the statistics-related features you want are available in the R language, including some complex icons. The code in the Machine learning direct

) Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell. A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA. (notice here not to be

its API is difficult to use. (Project address: Https://github.com/shogun-toolbox/shogun)2, KerasKeras is a high-level neural network API that provides a Python deep learning library. For any beginner, this is the best choice for machine learning because it provides a simpler way to express neural networks than other l

A bunch of online searches, and finally the links and differences between these concepts are summarized as follows: 1. Data mining: Mining is a very broad concept. It literally means digging up useful information from tons of data. This work bi (business intelligence) can be done, data analysis can be done, even market operations can be done. Using Excel to analyze the data and discover some useful information, the process of guiding your business through this information is also the process of