Deep Learning: A Case Study for Image Recognition Using Transfer Learning

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Author(s): Semra Erpolat Taşabat (Mimar Sinan Fine Art University, Turkey & Bahçeşehir University, Turkey)and Olgun Aydin (Gdansk University of Technology, Poland)
Copyright: 2021
Pages: 26
Source title: Artificial Neural Network Applications in Business and Engineering
Source Author(s)/Editor(s): Quang Hung Do (University of Transport Technology, Vietnam)
DOI: 10.4018/978-1-7998-3238-6.ch008

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

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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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