Search Results for "rapid"

...PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba. Based on one of the most widely-used Python tensor libraries: Theano.

...GLSLmath has been inspired by the glm and slmath libraries, which aim to mimic GLSL, but in contrast to those GLSLmath does not focus on a complete conforming implementation of GLSL. It rather aims to provide a convenient single header file that implements the most commonly used subset of GLSL so that it is easy to use for rapid prototyping and GLSL teaching purposes. It is used at the Georg-Simon Ohm University of Applied Sciences in Nuremberg for teaching computer graphics courses.

SPINA is software for the rapid, reliable and accurate determination of constant structure parameters of endocrine feedback control systems. It is a class of static function tests that require nothing more than steady-state concentrations of hormones or metabolites obtained in vivo. The first version of this cybernetic approach had been developed for the evaluation of the functional status of the thyroid gland, i.e. thyroid's secretory capacity (gain of thyroid, GT or SPINA-GT) and sum activity of peripheral deiodinases (GD or SPINA-GD). ...

Tiny Flash ActionScript Speed math program to train rapid multiplication and addition. Good for any age who wants to improve arithmetic and general maths skills. Quick mathematics is useful.

...With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model.