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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Deep Neural Evolution - Deep Learning with Evolutionary Computation (Hardcover, 1st ed. 2020)
Loot Price: R5,331
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Deep Neural Evolution - Deep Learning with Evolutionary Computation (Hardcover, 1st ed. 2020)
Series: Natural Computing Series
Expected to ship within 10 - 15 working days
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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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