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A Review on Basic Deep Learning Technologies and Applications

  • Tejashri PatilEmail author
  • Sweta Pandey
  • Kajal Visrani
Conference paper
  • 21 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 52)

Abstract

Deep learning is a rapidly developing area in data science research. Deep learning is basically a mix of machine learning and artificial intelligence. It proved to be more versatile, inspired by brain neurons, and creates more accurate models compared to machine learning. Yet, due to many aspects, making theoretical designs and conducting necessary experiments are quite difficult. Deep learning methods play an important role in automated systems of perception, falling within the framework of artificial intelligence. Deep learning techniques are used in IOT applications such as smart cities, image recognition, object detection, text recognition, bioinformatics, and pattern recognition. Neural networks are used for decision making in both machine learning and deep learning, but the deep learning framework here is quite different, using several nonlinear layers that generate complexity to obtain more precision, whereas a machine learning system is implemented linearly. In the present paper, those technologies were explored in order to provide researchers with a clear vision in the field of deep learning for future research.

Keywords

Deep learning Neural network Activation function Accuracy Loss function Weight Machine learning 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.SSBT’s College of Engineering and TechnologyJalgaonIndia

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