A daily deep dive into ml topics, coding problems, and platform features from PixelBank . Topic Deep Dive: Pooling From the CNNs & Sequence Models chapter Introduction to Pooling Pooling is a crucial concept in Convolutional Neural Networks (CNNs) , a type of Deep Learning model used for image and video processing. It is a technique used to reduce the spatial dimensions of an image, while retaining the most important features. This is essential in Machine Learning because it helps to decrease the number of parameters in the model, thereby reducing the risk of overfitting and improving the model's ability to generalize. The primary goal of Pooling is to downsample the feature maps generated by the convolutional layers . This is done by dividing the feature maps into smaller regions, called pooling regions , and selecting the most representative value from each region. The selected value is then used to represent the entire region, effectively reducing the spatial dimensions of the feature map.…