Understanding the Convolutional Neural Network & it’s Research Aspects in Deep Learning
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Convolutional Neural Network (CNN) is the most important Deep Neural Network (DNN) architecture to implement the Deep Learning’s application of data and pattern representation in an effective and efficient manner. It uses the idea of animal’s visual cortex organization to achieve connectivity pattern between its neurons. A receptive field is a restricted part of the space where a respond to stimuli are done by the individual cortical neurons. The main motivation behind the development of convolutional networks is the biological processes and CNNs are considered as the multilayer perceptrons’ variations that are designed for the purpose of providing the minimal usage of pre-processing. The major applications of convolutional neural network include image recognition, natural language processing, recommender systems and video recognition. We have tried to put an honest effort in this paper to analyze the Convolutional Neural Network (CNN) and the various developments made in its area of research.
Authors and Affiliations
Manish Kumar Singh, Prof. G S Baluja, Dr. Dinesh Prasad Sahu
Keywords
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