multiple layers

Multiple layers in neural networks refer to the arrangement of interconnected nodes, or neurons, that process input data through various transformations. Each layer consists of multiple neurons that apply specific weights and biases to the input, generating outputs that serve as inputs for subsequent layers. This architecture allows for the modeling of complex relationships within data, enabling the network to learn intricate patterns. The use of multiple layers enhances the network’s capacity to generalize from training data, making it effective for tasks such as classification and regression. Overall, multiple layers are fundamental to the design of deep learning models.