Convolution Neural Network: Architecture, Applications, and Recent Trends

View Sample PDF

Author(s): Kalyani N. Satone (Datta Meghe Institute of Higher Education and Research, India), Chitra A. Dhawale (Datta Meghe Institute of Higher Education and Research, India)and Pranjali B. Ulhe (Datta Meghe Institute of Higher Education and Research, India)
Copyright: 2023
Pages: 24
Source title: Scalable and Distributed Machine Learning and Deep Learning Patterns
Source Author(s)/Editor(s): J. Joshua Thomas (UOW Malaysia KDU Penang University College, Malaysia), S. Harini (Vellore Institute of Technology, India)and V. Pattabiraman (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-6684-9804-0.ch008

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

Purchase

View Convolution Neural Network: Architecture, Applications, and Recent Trends on the publisher's website for pricing and purchasing information.

Abstract

Convolutional neural network (CNN) carries spatial information—not all nodes in one layer are fully connected to nodes in the next layer; weights are shared. The main goal of CNN is to process large image pixel matrix and try to reduce high matrix dimensions without losing information, and to simplify the network architecture with weight sharing, reducing the number of trainable parameters in the network, which helped the model to avoid overfitting and as well as to improved generalization and still to give high performance with desired accuracy. So, CNN has become dominant in various computer vision tasks and is attracting interest across a variety of domains involving image processing. This chapter focuses on the foundation of CNN, followed by architecture of CNN, activation functions, applications, and recent trends in CNN.

The IRMA Community

Research IRM

Convolution Neural Network: Architecture, Applications, and Recent Trends

Purchase

Abstract

Related Content

IRMA Sponsors