000			03478nam a22004575i 4500
001			978-3-319-94463-0
003			DE-He213
005			20240111132923.0
007			cr nn 008mamaa
008			180825s2018 gw | s |||| 0|eng d
020			_a9783319944630
050		4	_aQ334-342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aAggarwal, Charu C. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aNeural Networks and Deep Learning _h[electronic resource] : _bA Textbook / _cby Charu C. Aggarwal.
250			_a1st ed. 2018.
300			_aXXIII, 497 p. 139 illus., 11 illus. in color. _bonline resource.
500			_aAcceso multiusuario
505	0		_a1 An Introduction to Neural Networks -- 2 Machine Learning with Shallow Neural Networks -- 3 Training Deep Neural Networks -- 4 Teaching Deep Learners to Generalize -- 5 Radical Basis Function Networks -- 6 Restricted Boltzmann Machines -- 7 Recurrent Neural Networks -- 8 Convolutional Neural Networks -- 9 Deep Reinforcement Learning -- 10 Advanced Topics in Deep Learning.
520			_aThis book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
541			_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023.
650		0	_aArtificial intelligence.
650		0	_aComputers.
650		0	_aMicroprocessors.
650	1	4	_aArtificial Intelligence.
650	2	4	_aInformation Systems and Communication Service.
650	2	4	_aProcessor Architectures.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319944623
776	0	8	_iPrinted edition: _z9783319944647
776	0	8	_iPrinted edition: _z9783030068561
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-94463-0
942			_cLIBRO_ELEC
999			_c242867 _d242866