Symbiotic-Computing-Laboratory/zero2neuro: v0.7.2
Authors/Creators
- 1. University of Oklahoma
- 2. Symbiotic Computing Laboratory
Description
Release v0.7.2
Abstract
Zero2Neuro is a no-code toolbox for constructing, training and evaluating Deep Neural Network (DNN) models for a wide range of modeling problems. This package provides easy-to-use solutions for:
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Loading data stored in a variety of formats (including the common Comma Separated Values format), and configuring the data for use in DNN training and evaluation.
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Creation of Deep Neural Network models from several flexible DNN model schemata, including fully-connected networks (FCNs), convolutional neural networks (CNNs), and U-Nets. The user specifies the structural details of their specific model.
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A standard DNN training and evaluation engine. This engine supports the production of result reports in various formats, including hooks for Weights and Biases.
Files
Symbiotic-Computing-Laboratory/zero2neuro-v0.7.2.zip
Files
(11.8 MB)
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md5:56341df704035b054dffc491ad7d4ae7
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Additional details
Additional titles
- Alternative title (English)
- Zero2Neuro: a No-Code Deep Neural Network Toolbox for Constructing, Training, and Evaluating Deep Neural Network Models
Related works
- Is supplement to
- Software: https://github.com/Symbiotic-Computing-Laboratory/zero2neuro/tree/v0.7.2 (URL)
Funding
- U.S. National Science Foundation
- AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES) 2019758
Software
- Repository URL
- https://github.com/Symbiotic-Computing-Laboratory/zero2neuro
- Development Status
- Active