000			02066nam a2200337 i 4500
001			CR9781108955652
003			UkCbUP
005			20240906181142.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			200626s2021||||enk o ||1 0|eng|d
020			_a9781108955652 (ebook)
020			_z9781108845359 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050		4	_aQ325.5 _b.B35 2021
082	0	4	_a006.31 _223
100	1		_aBaldi, Pierre, _eauthor.
245	1	0	_aDeep learning in science / _cPierre Baldi.
264		1	_aCambridge : _bCambridge University Press, _c2021.
300			_a1 online resource (xiv, 371 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 22 Feb 2021).
520			_aThis is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.
650		0	_aMachine learning.
650		0	_aScience _xTechnological innovations.
776	0	8	_iPrint version: _z9781108845359
856	4	0	_uhttps://doi.org/10.1017/9781108955652
942			_2ddc _cEB
999			_c9234 _d9234