000			05669nam a2200697 i 4500
001			9780750349574
003			IOP
005			20251030124215.0
006			m eo d
007			cr cn |||m|||a
008			231201s2023 enka fob 000 0 eng d
020			_a9780750349574 _qebook
020			_a9780750349567 _qmobi
020			_z9780750349550 _qprint
020			_z9780750349581 _qmyPrint
024	7		_a10.1088/978-0-7503-4957-4 _2doi
035			_a(CaBNVSL)thg00083690
035			_a(OCoLC)1412322926
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQC524b.R346 2023eb
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
100	1		_aRaeisi, Sadegh, _eauthor. _996570
245	1	0	_aMachine learning for physicists : _ba hands-on approach / _cSadegh Raeisi, Sedighe Raeisi.
264		1	_aBristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : _bIOP Publishing, _c[2023]
300			_a1 online resource (various pagings) : _billustrations (some color).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_a[IOP release $release]
490	1		_aIOP ebooks. [2023 collection]
500			_a"Version: 20231101"--Title page verso.
504			_aIncludes bibliographical references.
505	0		_a1. Preliminaries -- 1.1. Python -- 1.2. GitHub library -- 1.3. Datasets
505	8		_a2. Introduction -- 2.1. What is machine learning? -- 2.2. Applications of machine learning -- 2.3. Different types of machine learning -- 2.4. Structure of the book
505	8		_apart I. Supervised learning. 3. Supervised learning -- 3.1. Definitions, notations, and problem statement -- 3.2. Ingredients of supervised learning -- 3.3. Model evaluation and model selection -- 3.4. Summary -- 3.5. Questions
505	8		_a4. Neural networks -- 4.1. Introduction to neural networks -- 4.2. Training neural networks -- 4.3. Libraries for working with neural networks -- 4.4. Summary
505	8		_a5. Special neural networks -- 5.1. Convolutional neural network (CNN) -- 5.2. Time-series and recurrent neural networks (RNNs) -- 5.3. Graph neural network -- 5.4. Summary
505	8		_apart II. Unsupervised learning. 6. Unsupervised learning -- 6.1. Clustering -- 6.2. Anomaly detection -- 6.3. Dimensionality reduction -- 6.4. Summary
505	8		_a7. Generative models -- 7.1. Maximum likelihood estimation -- 7.2. Restricted Boltzmann machines -- 7.3. Generative adversarial networks (GAN) -- 7.4. Summary.
520	3		_aThis book presents machine learning (ML) concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics but little in the way of specialized computer science background. All chapters start with a simple introduction to the basics and the foundations, followed by some examples, and then proceeds to provide concrete examples with associated codes from a GitHub repository. Many of the code examples provided can be used as is or with suitable modification by the students for their own applications.
521			_aAdvanced undergraduate and graduate students in the physical sciences and their lecturers.
530			_aAlso available in print.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
545			_aSadegh Raeisi has a background in quantum computing and quantum information science. He completed his MSc at the University of Calgary and his PhD at the Institute for Quantum Computing at the University of Waterloo, as well as a Postdoc at the Max Planck Institute for the Science of Light in Erlangen. He then moved back to his home country and has held a faculty position since 2017. With about 18 years of research experience within the field of quantum computing, Sadegh is probably most recognized for his pioneering works on macroscopic quantumness and algorithmic cooling, including finding the cooling limit of heat-bath algorithmic cooling (HBAC) techniques which was an open problem for a decade, and for inventing the Blind HBAC technique, which is the optimal and practical HBAC technique. Sedighe Raeisi has a background in high-energy physics, nonlinear dynamics and chaotic systems. She holds a PhD from Ferdowsi University of Mashhad where she also worked for two years as a lecturer after graduation. Her areas of expertise include machine learning and deep learning with special focus on natural language processing (NLP), machine vision, graph neural networks and time series forecasting. She is currently working as a data scientist in the research and development division of Iran's largest telecommunications company.
588	0		_aTitle from PDF title page (viewed on December 1, 2023).
650		0	_aPhysics _xData processing. _996571
650		0	_aMachine learning. _955059
650		7	_aArtificial intelligence. _2bicssc _958441
650		7	_aCOMPUTERS / Artificial Intelligence / General. _2bisacsh _996572
700	1		_aRaeisi, Sedighe, _eauthor. _996573
710	2		_aInstitute of Physics (Great Britain), _epublisher. _917702
776	0	8	_iPrint version: _z9780750349550 _z9780750349581
830		0	_aIOP (Series). _pRelease 23.
830		0	_aIOP ebooks. _p2023 collection.
856	4	0	_uhttps://iopscience.iop.org/book/mono/978-0-7503-4957-4
942			_cE-Books
999			_c68380 _d68380