How work CNN?

Original article was published by Dr. Jaypalsing Kayte on Artificial Intelligence on Medium


A Convolutional Neural Network (CNN) is a form of artificial neural network that is explicitly designed to process pixel data used in image recognition and processing.

Effective image analysis, artificial intelligence ( AI) using deep learning to perform both generative and descriptive tasks are CNNs, mostly using machine vision that involves image and video recognition, along with suggested systems and processing of natural language (NLP).

CNN Layers

A neural network is a hardware and/or software device patterned in the human brain following the activity of neurons. For image recognition, conventional neural networks are not suitable and images must be fed into reduced-resolution bits. More like the frontal lobe, the region responsible for interpreting visual information in humans and other animals, CNN has the “neurons” organised. The neuron layers are structured in such a way that the entire visual field is filled, preventing the conventional neural networks’ piecemeal image processing issue.

A CNN uses a device optimised for decreased computing needs, much like a multilayer perceptron. The CNN layers consist of an input layer , an output layer, and a hidden layer containing several convolutionary layers, pooling layers, completely related layers, and layers of normalisation. Removing constraints and increasing the performance of image processing results in a method that is much more reliable and faster for image processing and natural language processing trains.

Result of CNN