Pooling Layers

Pooling layers are a component of neural networks used to reduce the size of data while preserving important information. Think of them as a way to summarize or condense features in an image or other data, helping the model focus on key patterns. For example, a max pooling layer takes small regions of an image and records only the brightest (maximum) value, reducing details but keeping essential features. This process makes the network more efficient, reduces computational load, and helps prevent overfitting, ultimately improving the model's ability to recognize meaningful patterns in the data.