What is the function of the pooling layer in a Convolutional Neural Network (CNN)?

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In a Convolutional Neural Network (CNN), the pooling layer brings an important improvement in reducing the spatial dimensions of the input features while retaining important information.

Pooling layers are typically inserted after convolutional layers to progressively down-sample the feature maps, which helps in reducing the computational complexity of the network, prevents overfitting, and makes the model more robust to variations in the input data (such as shifts, distortions, or noise).

Key Functions of the Pooling Layer:

1. Spatial Dimension Reduction (Down-sampling):

Function: The pooling layer reduces the size (dimensions) of the feature maps produced by the convolutional layers, thereby reducing the number of parameters and computations in the network. This is essential for managing large inputs…