Which layer in a neural network enables the model to learn complex patterns?

The hidden layer is pivotal in enabling a neural network to learn complex patterns. In a neural network, the hidden layer(s) sit between the input layer and the output layer. These layers consist of multiple neurons that apply transformations to the inputs received from the previous layers, allowing the network to extract higher-level features and intricate relationships within the data.

As data passes through the hidden layers, each neuron processes the input it receives, often with the aid of activation functions that introduce non-linearity into the learning process. This non-linearity is crucial because it allows the network to approximate complex functions and recognize patterns that are not linearly separable. The configuration and activation of neurons in hidden layers determine how effectively the model learns these patterns, enabling it to generalize from the training data while making predictions on unseen data.

The other layers serve distinct purposes in the overall structure of the neural network. The input layer is responsible for receiving the initial data but does not engage in learning patterns. The output layer, on the other hand, produces the final predictions based on the transformations performed in the hidden layers. The activation layer, while important for introducing non-linearities, is typically part of the hidden and output layers rather than a distinct layer itself.

In summary, the hidden