000			03515nam a22004095i 4500
999			_c368472 _d368472 _x1
001			368472
003			ES-MaUEC
005			20230102121737.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220409s2022 sz | s |||| 0|eng d
020			_a9783030899295
024	7		_a10.1007/978-3-030-89929-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2022 EB
100	1		_aSalem, Fathi M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683499
245	1	0	_aRecurrent Neural Networks : _bFrom Simple to Gated Architectures _cby Fathi M. Salem
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XX, 121 páginas) _b26 ilustraciones, 24 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
505	0		_aIntroduction -- 1. Network Architectures -- 2. Learning Processes -- 3. Recurrent Neural Networks (RNN) -- 4. Gated RNN: The Long Short-Term Memory (LSTM) RNN -- 5. Gated RNN: The Gated Recurrent Unit (GRU) RNN -- 6. Gated RNN: The Minimal Gated Unit (MGU) RNN.
520			_aThis textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author's approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications. Explains the intricacy and diversity of recurrent networks from simple to more complex gated recurrent neural networks; Discusses the design framing of such networks, and how to redesign simple RNN to avoid unstable behavior; Describes the forms of training of RNNs framed in adaptive non-convex optimization with dynamics constraints.
988			_aSpringer_Engineering_2022
650		7	_2embne _9678664 _aRedes neuronales artificiales
776	0	8	_iPrinted edition: _z9783030899288
776	0	8	_iPrinted edition: _z9783030899301
776	0	8	_iPrinted edition: _z9783030899318
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-89929-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _eIG _zSI