Deep learning theory simplified

Adilya Bakambekova; Alex James Pappachen

doi:10.1007/978-3-030-14524-8_3

Deep learning theory simplified

Adilya Bakambekova, Alex James Pappachen

Department of Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Deep Learning is a promising field of Artificial Intelligence algorithms that have proven to be capable of solving a wide range of tasks including classification, object detection, regression, face recognition, augmented and virtual reality, self-driving cars and many more. This chapter introduces the reader to Deep Learning, its basic principles, and applications. It covers the essential elements of any Deep Learning system, as well as explains how to connect these elements to form a neural network. The reader will understand the reasoning behind the Deep Learning and why it is so useful nowadays. The training algorithm of the neural network is also covered in this chapter.

Original language	English
Title of host publication	Modeling and Optimization in Science and Technologies
Publisher	Springer Verlag
Pages	41-55
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-030-14524-8_3
Publication status	Published - Jan 1 2020

Publication series

Name	Modeling and Optimization in Science and Technologies
Volume	14
ISSN (Print)	2196-7326
ISSN (Electronic)	2196-7334

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ASJC Scopus subject areas

Modelling and Simulation
Medical Assisting and Transcription
Applied Mathematics

Cite this

Bakambekova, A., & James Pappachen, A. (2020). Deep learning theory simplified. In Modeling and Optimization in Science and Technologies (pp. 41-55). (Modeling and Optimization in Science and Technologies; Vol. 14). Springer Verlag. https://doi.org/10.1007/978-3-030-14524-8_3

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Access to Document

10.1007/978-3-030-14524-8_3