Deep Learning

Semra Erpolat Tasabat; Olgun Aydin

doi:10.4018/978-1-7998-3238-6.ch008

Deep Learning

Abstract

Deep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities, security, automated machines. In this chapter, brief information about DL theory is given, advantages and disadvantages of deep learning are discussed, most used types of DNN are mentioned, popular DL architectures and frameworks are glanced and aimed to build smart systems for the finance and real estate domains. Finally, a case study about image recognition using transfer learning is developed.

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Authors (2)

Semra Erpolat Tasabat
Olgun Aydin dr

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Category:: Monographic publication
Type:: rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Title of issue:: Artificial Neural Network Applications in Business and Engineering strony 171 - 196
Language:: English
Publication year:: 2021
Bibliographic description:: Erpolat Tasabat S., Aydin O.: Deep Learning// Artificial Neural Network Applications in Business and Engineering/ : , 2021, s.171-196
DOI:: Digital Object Identifier (open in new tab) 10.4018/978-1-7998-3238-6.ch008
Verified by:: Gdańsk University of Technology