neuralparameters
Neuralparameters are the adjustable values that define the behavior of a neural network. They are learned from data during training and stored in the model as tensors. They differ from hyperparameters, which are settings chosen before training to govern the learning process rather than the model’s predictions.
In feedforward networks, the primary neuralparameters are the weight matrices W^(l) and bias vectors b^(l) for
These parameters are optimized by gradient-based learning. Backpropagation computes gradients of a loss with respect to
The total number of neuralparameters largely determines model capacity and memory footprint. They are saved with
Notes: Not all components are strictly parameters in every context; some models include fixed components or