Basics of the Convolutional Neural Network (CNN): Part I

A convolutional neural network (CNN) tries to detect multiple patterns in different regions of an image using a receptive field, which is the area that a neuron sees when processing data. Here are some key features of a CNN:

• A CNN is a special type of neural network—generally used for image data—that can extract features from an image so that the computer can identify its content.

• The intuition behind CNN is to reduce the input size while increasing the depth (equal to the number of channels) in the network.

• A CNN uses convolution instead of general matrix multiplication.

• Instead of feeding pixels to a neural network, we feed features to CNN.

Why are CNNs needed over ANNs for images?

CNNs are preferred over ANNsAn artificial neural network (ANN) is a computational model inspired by the structure of biological neural networks, designed to recognize patterns and make decisions by processing input data through layers of interconnected nodes or “neurons.” for image processing tasks because they can effectively capture spatial hierarchies and local patterns in images due to their convolutional layers and weight-sharing architecture. This enables CNNs to learn more complex features while requiring fewer parameters, making them more efficient and accurate in handling image data.

Problems with neural networks

Let’s learn in detail why CNNs perform better than ANNs for image data.

Rotation/position invariance