05494nam a2200649 i 45000010014000000030004000140050017000180060019000350070015000540080041000690200025001100200024001350200025001590200027001840240035002110350025002460350022002710400040002930500023003330720016003560720023003721000029003952450093004242640098005173000071006153360021006863370026007073380032007334900027007654900034007925000043008265040041008695050076009105050169009865050232011555050163013875050176015505050155017265050164018815200750020455210095027955300029028905380036029195380078029555451345030335880060043786500030044386500022044686500037044906500060045277000030045877100054046177760049046718300031047208300034047518560059047859780750349574IOP20251030124215.0m    eo  d        cr cn |||m|||a231201s2023    enka   fob    000 0 eng d  a9780750349574qebook  a9780750349567qmobi  z9780750349550qprint  z9780750349581qmyPrint7 a10.1088/978-0-7503-4957-42doi  a(CaBNVSL)thg00083690  a(OCoLC)1412322926  aCaBNVSLbengerdacCaBNVSLdCaBNVSL 4aQC524b.R346 2023eb 7aUYQ2bicssc 7aCOM0040002bisacsh1 aRaeisi, Sadegh,eauthor.10aMachine learning for physicists :ba hands-on approach /cSadegh Raeisi, Sedighe Raeisi. 1aBristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) :bIOP Publishing,c[2023]  a1 online resource (various pagings) :billustrations (some color).  atext2rdacontent  aelectronic2isbdmedia  aonline resource2rdacarrier1 a[IOP release $release]1 aIOP ebooks. [2023 collection]  a"Version: 20231101"--Title page verso.  aIncludes bibliographical references.0 a1. Preliminaries -- 1.1. Python -- 1.2. GitHub library -- 1.3. Datasets8 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 book8 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. Questions8 a4. Neural networks -- 4.1. Introduction to neural networks -- 4.2. Training neural networks -- 4.3. Libraries for working with neural networks -- 4.4. Summary8 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. Summary8 apart II. Unsupervised learning. 6. Unsupervised learning -- 6.1. Clustering -- 6.2. Anomaly detection -- 6.3. Dimensionality reduction -- 6.4. Summary8 a7. Generative models -- 7.1. Maximum likelihood estimation -- 7.2. Restricted Boltzmann machines -- 7.3. Generative adversarial networks (GAN) -- 7.4. Summary.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.  aAdvanced undergraduate and graduate students in the physical sciences and their lecturers.  aAlso available in print.  aMode of access: World Wide Web.  aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.  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.0 aTitle from PDF title page (viewed on December 1, 2023). 0aPhysicsxData processing. 0aMachine learning. 7aArtificial intelligence.2bicssc 7aCOMPUTERS / Artificial Intelligence / General.2bisacsh1 aRaeisi, Sedighe,eauthor.2 aInstitute of Physics (Great Britain),epublisher.08iPrint version:z9780750349550z9780750349581 0aIOP (Series).pRelease 23. 0aIOP ebooks.p2023 collection.40uhttps://iopscience.iop.org/book/mono/978-0-7503-4957-4