What is Convolutional Neural Network in Machine Learning?

A Convolutional neural network (CNN) is a neural network that has one or more convolutional layers and is used mainly for image processing, classification, segmentation, and also for other auto-correlated data. A convolution is essentially sliding a filter over the input.

Convolutional Neural Network(CNN) :

  • A convolutional neural network, or CNN, is a deep learning neural network sketched for processing structured arrays of data such as portrayals.
  • CNN are very satisfactory at picking up on design in the input image, such as lines, gradients, circles, or even eyes and faces.
  • This characteristic that makes convolutional neural network so robust for computer vision.
  • CNN can run directly on a underdone image and do not need any preprocessing.
  • A convolutional neural network is a feed forward neural network, seldom with up to 20.
  • The strength of a convolutional neural network comes from a particular kind of layer called the convolutional layer.
  • CNN contains many convolutional layers assembled on top of each other, each one competent of recognizing more sophisticated shapes.
  • With three or four convolutional layers it is viable to recognize handwritten digits and with 25 layers it is possible to differentiate human faces.
  • The agenda for this sphere is to activate machines to view the world as humans do, perceive it in a alike fashion and even use the knowledge for a multitude of duty such as image and video recognition, image inspection and classification, media recreation, recommendation systems, natural language processing, etc.