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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Statistical Field Theory for Neural Networks (Paperback, 1st ed. 2020)
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Statistical Field Theory for Neural Networks (Paperback, 1st ed. 2020)
Series: Lecture Notes in Physics, 970
Expected to ship within 9 - 15 working days
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This book presents a self-contained introduction to techniques from
field theory applied to stochastic and collective dynamics in
neuronal networks. These powerful analytical techniques, which are
well established in other fields of physics, are the basis of
current developments and offer solutions to pressing open problems
in theoretical neuroscience and also machine learning. They enable
a systematic and quantitative understanding of the dynamics in
recurrent and stochastic neuronal networks. This book is intended
for physicists, mathematicians, and computer scientists and it is
designed for self-study by researchers who want to enter the field
or as the main text for a one semester course at advanced
undergraduate or graduate level. The theoretical concepts presented
in this book are systematically developed from the very beginning,
which only requires basic knowledge of analysis and linear algebra.
General
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