000			02002cam a22003135i 4500
001			22934169
005			20250527154000.0
008			230119s2023 mau 000 0 eng
010			_a 2023931034
020			_a9783111024318
020			_z9783111025551
020			_z9783111025803
040			_aDLC _beng _erda _cDLC
042			_apcc
082			_a006.31 BER-L
100	1		_aBerlyand, Leonid
245	1	0	_aMathematics of deep learning : _ban introduction / _cLeonid Berlyand and Pierre-Emmanuel Jabin
260			_aBerlin _bWalter De Gruyter _c2023
300			_a126 p.
490	0		_aDe Gruyter Textbook
500			_aThe goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning" for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.
650			_aComputer -- Intelligence (AI) and Semantics
650			_aDeep learning (Machine learning) Mathematics
650			_aNeural networks (Computer science) Mathematics
700	1		_aJabin, Pierre-Emmanuel, _eauthor.
906			_a0 _bibc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc
955			_bxd05 2023-01-19
999			_c93516 _d93516