Weights and biases are core parameters in neural networks that determine how inputs are transformed into outputs. Weights connect neurons across layers, scaling input values to learn patterns.
Biases are added to the weighted inputs, enabling the model to shift activation functions. This flexibility allows the network to represent a broader range of relationships. For example, without biases, a neuron might struggle to model complex patterns.
During training, weights and biases are updated iteratively through backpropagation and optimization, minimizing the loss function to improve model accuracy.