Search Results for "production test"

Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor ML models from validation to production. It works with tabular, text data and embeddings.

The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. ...

...It includes implementations of standard machine learning algorithms (such as regression, classification, etc.), scripts for data loading and preprocessing, and evaluation routines (e.g. accuracy, error metrics). Because it is structured as homework or practice material, the code is likely intended more for didactic use than for production deployment. It may contain comments, example datasets, and perhaps test scripts. The repository does not seem to be heavily maintained as a software project; rather, it functions as a library of solved problems and educational examples. The project is useful if you want working MATLAB examples of classic ML techniques, to study, adapt, or compare with your own implementations.

Multiple Back-Propagation is an open source software application for training neural networks with the backpropagation and the multiple back propagation algorithms. Currently this project is also hosted at http://code.google.com/p/multiplebackpropagation

Application implements models described by classical mathematical equation for in vitro deposition prediction based on characteristics of formulation and assay conditions. This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012. Published article: https://www.dovepress.com/empirical-modeling-of-the-fine-particle-fraction-fornbspcarrier-based--peer-reviewed-fulltext-article-IJN

This program runs on a laptop with a wireless card. It scan for wireless access points (WAPs) and notes their Base Station ID or BSSID and their Received Signal Strength Indicator or RSSI. This data, along with an identifier for the physical location or 'ROOM', and the name of the computer it is being run on are stored as an ARFF file, which is compatible with programs such as WEKA and RAPIDMINER.

BCAR is a library for the associative classification, which denotes "Boosting Class Association Rules". BCAR provides a general tool for classification tasks with various types of input data.