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Deep Neural Evolution

Name: Deep Neural Evolution
Brand: Springer Verlag, Singapore
SKU: 9789811536847
Price: 775.24 PLN
Availability: InStock

Deep Learning with Evolutionary Computation

Autor: Hitoshi Iba, Nasimul Noman

Język:

Oprawa: Twarda

Wydawca: Springer Verlag, Singapore

Dostępność: Dostępna u dostawcy

Wysyłamy za 10-18 dni

775.24 zł

This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary c...

Informacje o książce

Autor

Hitoshi Iba, Nasimul Noman

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2020

strony

438

EAN

9789811536847

Enbook ID

25686813

Wydawca

Springer Verlag, Singapore

Waga

840

Wymiary

155 x 235 x 30

Pełny opis

This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research -from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.

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Deep Learning with Evolutionary Computation

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