Steps to apply machine learning to your data

Any machine learning task can be broken down into a series of more manageable steps. 1. Collecting data: Whether the data is written on paper, recorded in text files and spreadsheets, or stored in an SQL database, you will need to gather it in an electronic format suitable for analysis. This data will serve as the learning material an algorithm …

Thống kê sử dụng R

Bài giảng thống kê trong y học sử dụng ngôn ngữ R. Chuong 13. Phan tich su kien Chuong 14. Phan tich tong hop Chuong 15. Uoc tinh co mau Chuong 16. Lap trinh va ham Chuong 17. Mot so lenh R thong dung Chuong 18. Thuat ngu Chuong 19. Tai lieu tham khao va doc them _Muc luc Chuong …

Deep Learning How I Did It: Merck 1st place interview

Posted on November 1 2012 by George Dahl What was your background prior to entering this challenge? We are a team of computer science and statistics academics. Ruslan Salakhutdinov and Geoff Hinton are professors at the University of Toronto. George Dahl and Navdeep Jaitly are Ph.D. students working with Professor Hinton. Christopher “Gomez” Jordan-Squire is in the mathematics Ph.D. program …

Some financial Datasets

Clean Credit Scoring Credit Rate Financial Data AI Credit Scoring China company firm bankruptcy Japan solvent 18-Rating

Predicting Stock Market Returns

Predicting Stock Market Returns: R Code of Chapter 3 (right-click here to save the code in a local file) ################################################### ### The Available Data ################################################### library(DMwR) data(GSPC) ################################################### ### Handling time dependent data in R ################################################### library(xts) x1 <- xts(rnorm(100),seq(as.POSIXct(“2000-01-01″),len=100,by=”day”)) x1[1:5] x2 <- xts(rnorm(100),seq(as.POSIXct(“2000-01-01 13:00″),len=100,by=”min”)) x2[1:4] x3 <- xts(rnorm(3),as.Date(c(‘2005-01-01′,’2005-01-10′,’2005-01-12’))) x3 x1[as.POSIXct(“2000-01-04”)] x1[“2000-01-05”] x1[“20000105”] x1[“2000-04”] x1[“2000-03-27/”] x1[“2000-02-26/2000-03-03”] x1[“/20000103”] mts.vals <- matrix(round(rnorm(25),2),5,5) …

Deep Learning

Deep learning is a set of algorithms in machine learning that attempt to learn layered models of inputs, commonly neural networks. The layers in such models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts Deep learning is just a buzzword for neural …

[2012] A hybrid feature selection for fault prediction

Software fault prediction plays a vital role in software quality assurance. Identifying the faulty modules helps to better concentrate on those modules and helps improve the quality of the software. With increasing complexity of software nowadays feature selection is important to remove the redundant, irrelevant and erroneous data from the dataset. In general, Feature selection is done mainly based on …