A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning
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ORIGINAL PAPER
A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning Shaveta Dargan1 · Munish Kumar1 · Maruthi Rohit Ayyagari2 · Gulshan Kumar3 Received: 11 November 2018 / Accepted: 26 May 2019 © CIMNE, Barcelona, Spain 2019
Abstract Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.
1 Introduction Machine learning is a subsection of Artificial Intelligence (AI) that imparts the system, the benefits to automatically learn from the concepts and knowledge without being explicitly programmed. It starts with observations such as the direct experiences to prepare for the features and patterns in data and producing better results and decisions in the future. Deep learning relies on the collection of machine learning algorithms which models highlevel abstractions in the data with multiple nonlinear
* Munish Kumar [email protected] 1
Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India
2
College of Business, University of Dallas, Irving, USA
3
Department of Computer Applications, Shaheed Bhagat Singh State Technical Campus, Ferozepur, Punjab, India
transformations. A deep learning technology works on the artificial neural
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