What are Convolutional and Pooling Layers in CNN?

Convolutional Layers:

These are the first layers in a CNN, and they can be thought of as “Filters” for an image. Just like Filters in Instagram detect our face, a convolutional layer detects features or filters such as edges in an image, wherever they might be present.

Pooling Layers:

The pooling layers mainly reduce the computational cost by reducing the spatial size of the image. The best way to describe it would be that it makes the grids of information smaller by taking a “lump-sum” of the images’ spatial resolution.

In this article, we are going to see how to  Define a Simple Convolutional Neural Network in PyTorch using Python. Convolutional Neural Networks(CNN) is a type of Deep Learning algorithm which is highly instrumental in learning patterns and features in images. CNN has a unique trait which is its ability to process data with a grid-like topology whereas a typical Artificial Neural Network(Dense or Sparse) generally takes input by flattening the tensors into a one-dimensional vector. This facilitates it to learn and differentiate between features in images, which when represented digitally are essentially a grid of numbers.

Convolutional Neural Networks are typically comprised of multiple layers. Usually, the initial layers are used to detect simple features such as edges, and complex features are detected down the line, as we go deeper into the network.

CNN has countless qualities that make it so suitable for processing images. Let’s take a look at some of them:-

• They require much less data pre-processing than other Deep Learning Algorithms.
• A  well-trained CNN model has the ability to learn and classify features in an image, which gives much better accuracy in the classification and detection of features in images.
• It can save a lot of computational resources by methods like increasing the convolutional and pooling layers.