Radal's visualization training is achieved in three steps:Select Model(from the template library or custom upload),Drag-and-drop restructuring(e.g., adding attention mechanisms),Real-time monitoring indicators(Accuracy/loss value). Compared to traditional coding training:
- Data set processingAutomatic recognition of CSV/JSON/text formats and optimization suggestions, no need for manual data cleansing
- parameterization: Adjust the number of layers and other parameters through the interface slider, instead of the command line operation
- output method: Direct export to ONNX/TensorFlow format after training, eliminating the need for format conversion.
Typical training time is reduced from several days for traditional development to 1-6 hours, depending on data size.
This answer comes from the articleRadal: a low-code platform for rapid fine-tuning and optimization of AI modelsThe
May not be reproduced without permission:AI productivity tools " How exactly does Radal's visual model training feature work? How does it differ from traditional methods?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil