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 each layer. In convolutional networks, these correspond to the kernel weights. In recurrent architectures, neuralparameters include recurrent weight matrices and input/output projection parameters. In transformer models, attention mechanisms introduce projection matrices for queries, keys, and values. Many networks also learn normalization parameters, such as gamma and beta in batch normalization, or scale and shift parameters in layer normalization.

These parameters are optimized by gradient-based learning. Backpropagation computes gradients of a loss with respect to each parameter, and optimizers such as stochastic gradient descent or Adam update their values. Initialization schemes matter for convergence, with common choices including Xavier/Glorot and He initialization. Regularization methods (for example, weight decay and dropout) influence the learned parameters and model generalization.

The total number of neuralparameters largely determines model capacity and memory footprint. They are saved with the model, enabling reuse in inference and transfer learning. Their size and structure affect training time, generalization, and susceptibility to overfitting. Techniques such as pruning, quantization, and low-rank factorization are used to reduce their footprint or improve efficiency.

Notes: Not all components are strictly parameters in every context; some models include fixed components or dynamic states, and some parameters may be shared across layers.