
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: 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. 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. A standard DNN training and evaluation engine. This engine supports the production of result reports in various formats, including hooks for Weights and Biases.
keras3, tensorflow, machine learning, deep neural networks, no-code toolbox for deep neural networks
keras3, tensorflow, machine learning, deep neural networks, no-code toolbox for deep neural networks
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
