Deep learning theory simplified

Adilya Bakambekova, Alex James Pappachen

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages41-55
Number of pages15
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameModeling and Optimization in Science and Technologies
Volume14
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

    Fingerprint

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