Better simulations of raindrop formation could help improve climate and weather models. This newsletter rocks. Get the most ...

David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

Logistic regression is a statistical method used to model binary outcome variables, such as whether a patient recovers or not, using a set of predictors. There are many competing methods for ...

Compared to other regression techniques, a well-tuned neural network regression system can produce the most accurate prediction model, says Dr. James McCaffrey of Microsoft Research in presenting this ...

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...

Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and to government agencies from the local to the national level. Weather forecasts zero to ten ...

Data Science combines scientific inquiry, statistical knowledge and computer programming with a focus on learning powerful insights from big data. Businesses use data to plan, evaluate, innovate, and ...

FSML (Fortran Statistics and Machine Learning) is a scientific toolkit consisting of common statistical and machine learning procedures, including basic statistics (e.g., mean, variance, correlation), ...

Abstract: Good data analysis is required for the optimal design of nuclear energy projects. However, due to financial or technical reasons, data cannot be collected regularly, which leads to missing ...

Note: This repository is retired and will not be ported to use TF2. However, you may use this as a reference in doing so. This paper was presented at the 2nd International Conference on Machine ...