Convolutional Neural Network (CNN) Fundamental Operational Survey

Abstract

A Convolutional Neural Network (CNN) is an algorithm of Deep Learning. It reads image as input. Allocate weights and biases to several objects in image. It identifies the differences from one image to another image. The pre-processing operation perform on CNN is less compare to other algorithms. CNN have the ability to gain filters. CNN are made up of multi-layer perceptron’s. It includes of input layer, output layer and hidden-layer. Hidden layer includes of convolution layer, ReLU layer, Pooling layer and fully-connected layer. CNN automatically detects the features without human interventions. It has more computational efficiency. CNN overcomes the limitations of other algorithms like if the images contains the large pixels then it’s not possible to build that much of neurons. This can be solved by fully-connected layer. In fully-connected layer all the neurons are connected to each other. All neuron’s are connected in all hidden layers of CNN. Some of the applications of CNN are Face recognition, Image classification, Analysing hand written documents and other documents, Weather reporting, Online advertisements, Natural distortion prediction, Earthquake prediction, Self-driving cars, Robots, Detection of some medical problems, Speech recognition, etc.