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- W3118228001 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." @default.
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- W3118228001 date "2021-01-01" @default.
- W3118228001 modified "2023-09-27" @default.
- W3118228001 title "Deep Learning" @default.
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- W3118228001 doi "https://doi.org/10.4018/978-1-7998-3238-6.ch008" @default.
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