What is the purpose of the hidden layers in a neural network?

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In a neural network, hidden layers play a crucial role in the model’s ability to learn and represent complex patterns within the data. Unlike the input and output layers, hidden layers are not directly visible in the final output but serve as intermediary computational layers that enable the network to model intricate relationships and non-linearities in the data. The hidden layers transform the input data into meaningful representations that the network can use to make accurate predictions.

Key Purposes of Hidden Layers:

1. Capturing Non-Linear Relationships:

Purpose: Hidden layers introduce non-linear transformations to the data by applying activation functions to the weighted sum of inputs. This allows the network to model complex, non-linear relationships between the features and the target variable, which cannot be captured by linear…