Open AccessDeep Learning Through Convolutional Neural Networks
Author(s) -
Ms. Pooja Kalange*,
Ms. Megha Desai
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3770.098319
Subject(s) - computer science , artificial intelligence , convolutional neural network , analytics , deep learning , machine learning , adaptability , segmentation , image processing , property (philosophy) , pattern recognition (psychology) , image (mathematics) , data science , ecology , philosophy , epistemology , biology
Deep learning which is associated with the basics of Machine Learning has become popular over the years because of its fast paced adaptability and ability to handle complex problems. Prior to this technology breakthrough traditional methods of machine learning were used in applications of Image processing and pattern recognition, and analytics .With the advent of CNNs it has become easy to combat complex learning problems using the property of specificity and accuracy in CNN architectures and methodologies. This paper gives an introductory insights in CNNs like the feed-forward propagation networks and Back propagation Networks. The paper explains steps followed by CNNs for classifying the input and generating a predefined output. It also explains evolution of multiple Image CNN architectures which find applications in multiple domains of Computer Science like Image Processing & Segmentation, Pattern Recognition & Predictive Analytics, Text Analytics to name a few .