Zero2Neuro: a No-Code Toolbox for Constructing, Training, and Evaluating Deep Neural Network Models
Authors/Creators
- 1. University of Oklahoma
Description
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-v0.6.3.zip
Files
(11.8 MB)
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md5:d68be81628f05d016631171a3db73936
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Additional details
Related works
- Is published in
- Software: https://github.com/Symbiotic-Computing-Laboratory/zero2neuro/tree/v0.6.3 (URL)
Funding
- U.S. National Science Foundation
- AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES) 2019758
- U.S. National Science Foundation
- OneOklahoma Cyberinfrastructure Initiative Artificial Intelligence Consultants (ONEOCII-AIC): Developing a pipeline of AI consultants and workforce training program. 2538234
Dates
- Available
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2026-05-21First official release
Software
- Repository URL
- https://github.com/Symbiotic-Computing-Laboratory/zero2neuro
- Programming language
- Python , Jupyter Notebook
- Development Status
- Active