000			03238nam a22004098a 4500
001			00012294
003			WSP
007			cr |nu|||unuuu
010			_a 2021024473
040			_aWSPC _beng _cWSPC
020			_a9789811237461 _q(ebook)
020			_a9811237468 _q(ebook)
020			_z9789811237454 _q(hbk.)
020			_z981123745X _q(hbk.)
042			_apcc
050	0	0	_aQC52 _b.E73 2021
072		7	_aSCI _x040000 _2bisacsh
072		7	_aCOM _x094000 _2bisacsh
072		7	_aSCI _x077000 _2bisacsh
082	0	0	_a530.0285 _223
049			_aMAIN
100	1		_aErdmann, Martin, _d1960 February 6- _921159
245	1	0	_aDeep learning for physics research _h[electronic resource] / _cMartin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany.
260			_aSingapore : _bWorld Scientific, _c[2021]
300			_a1 online resource (340 p.).
504			_aIncludes bibliographical references and index.
505	0		_aDeep learning basics. Scope of this textbook -- Models for data analysis -- Building blocks of neural networks -- Optimization of network parameters -- Mastering model building -- Standard architectures of deep networks. Revisiting the terminology -- Fully-connected networks: improving the classic all-rounder -- Convolutional neural networks and analysis of image-like data -- Recurrent neural networks: time series and variable input -- Graph networks and convolutions beyond Euclidean domains -- Multi-task learning, hybrid architectures, and operational reality -- Introspection, uncertainties, objectives. Interpretability -- Uncertainties and robustness -- Revisiting objective functions -- Deep learning advanced concepts. Beyond supervised learning -- Weakly-supervised classification -- Autoencoders: finding and compressing structures in data -- Generative models: data from noise -- Domain adaptation, refinement, unfolding -- Model independent detection of outliers and anomalies -- Beyond the scope of this textbook.
520			_a"A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded."-- _cProvided by publisher.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
650		0	_aPhysics _xData processing. _917226
650		0	_aPhysics _xResearch. _921160
650		0	_aMachine learning. _91831
655		0	_aElectronic books. _93294
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/12294#t=toc _zAccess to full text is restricted to subscribers.
942			_cEBK
999			_c72749 _d72749