Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. This paper, proposes the possibility of learning deep network structures and the relative study of their wide range of applications. CNNs use a variation of multilayer perceptron designed to require minimal preprocessing. The engineering of CNN is accomplished with neighborhood associations and tied weights overtaken by some type of pooling which brings about interpretation invariant highlights. Another advantage of CNNs is that they are less demanding to prepare and have numerous less parameters than completely associated systems with a similar number of concealed units. An Artificial Neural Network (ANN) with numerous concealed layers between the info and yield layers is a profound neural system (DNN). Deep learning structures of CNN, have been connected to fields of computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where the delivered comes about tantamount to and at times better than human specialists.

Keywords: Neural Network (CNN), Field-Programmable Gate Array (FPGA), Deep Neural Networks, Drop-out algorithm.