Configuration is the process of defining the settings and initial properties used to train, run, or deploy a model or system. In machine learning, configuration may include model architecture, data locations, hyperparameters, optimizer choices, and loss functions.
The process of assigning the initial property values used to train a model, including: - the model's composing layers - the location of the data - hyperparameters such as: - learning rate - iterations - optimizer - loss function In machine learning projects, configuration can be done through a special configuration file or using configuration libraries such as the following: - HParam - Gin - Fiddle