SLIM21

Sort Order Display Format Items / Page  
 
  Click the serial number on the left to view the details of the item.
 #  AuthorTitleAccn#YearItem Type Claims
1 Sch??tt, Kristof T Machine Learning Meets Quantum Physics I09716 2020 eBook  
2 Helias, Moritz Statistical Field Theory for Neural Networks I09583 2020 eBook  
3 Czischek, Stefanie Neural-Network Simulation of Strongly Correlated Quantum Systems I09120 2020 eBook  
4 Zhao, Haitao Feature Learning and Understanding I09105 2020 eBook  
5 Stark, Giordon The Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction I09004 2020 eBook  
6 Matthew Kirk Thoughtful machine learning: A Test-Driven Aproach 026224 2014 Book  
7 Nikhil Buduma Fundamentals of deep learning: designing next generation machine intelligence algorithms PP0025 Book  
8 Christopher M. Bishop Pattern recognition and machine learning 026213 2011 Book  
9 Ian Goodfellow Deep learning 026211 2017 Book  
10 Yuichiro Anzai Pattern recognition and machine learning 008160 1992 Book  
(page:1 / 2) [#11]    Next Page   Last Page 

1.    
No image available
TitleMachine Learning Meets Quantum Physics
Author(s)Sch??tt, Kristof T;Chmiela, Stefan;von Lilienfeld, O. Anatole;Tkatchenko, Alexandre;Tsuda, Koji;M??ller, Klaus-Robert
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXVI, 467 p. 137 illus., 125 illus. in color : online resource
Abstract NoteDesigning molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
ISBN,Price9783030402457
Keyword(s)1. Chemistry, Physical and theoretical 2. EBOOK 3. EBOOK - SPRINGER 4. MACHINE LEARNING 5. Numerical and Computational Physics, Simulation 6. PHYSICS 7. QUANTUM PHYSICS 8. Theoretical and Computational Chemistry
Item TypeeBook
Multi-Media Links
Please Click here for eBook
Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
I09716     On Shelf    

2.     
No image available
TitleStatistical Field Theory for Neural Networks
Author(s)Helias, Moritz;Dahmen, David
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXVII, 203 p. 127 illus., 5 illus. in color : online resource
Abstract NoteThis book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra
ISBN,Price9783030464448
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. MACHINE LEARNING 4. Mathematical Models of Cognitive Processes and Neural Networks 5. MATHEMATICAL STATISTICS 6. Neural networks (Computer science)?? 7. Neurosciences 8. Probability and Statistics in Computer Science 9. STATISTICAL PHYSICS 10. Statistical Physics and Dynamical Systems
Item TypeeBook
Multi-Media Links
Please Click here for eBook
Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
I09583     On Shelf    

3.     
No image available
TitleNeural-Network Simulation of Strongly Correlated Quantum Systems
Author(s)Czischek, Stefanie
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXV, 205 p. 51 illus., 48 illus. in color : online resource
Abstract NoteQuantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both
ISBN,Price9783030527150
Keyword(s)1. CONDENSED MATTER 2. CONDENSED MATTER PHYSICS 3. EBOOK 4. EBOOK - SPRINGER 5. MACHINE LEARNING 6. Mathematical Models of Cognitive Processes and Neural Networks 7. Neural networks (Computer science)?? 8. QUANTUM PHYSICS
Item TypeeBook
Multi-Media Links
Please Click here for eBook
Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
I09120     On Shelf    

4.     
No image available
TitleFeature Learning and Understanding : Algorithms and Applications
Author(s)Zhao, Haitao;Lai, Zhihui;Leung, Henry;Zhang, Xianyi
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXIV, 291 p. 126 illus., 109 illus. in color : online resource
Abstract NoteThis book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence
ISBN,Price9783030407940
Keyword(s)1. Computational Intelligence 2. Data-driven Science, Modeling and Theory Building 3. EBOOK 4. EBOOK - SPRINGER 5. ECONOPHYSICS 6. IMAGE PROCESSING 7. Image Processing and Computer Vision 8. MACHINE LEARNING 9. OPTICAL DATA PROCESSING 10. PATTERN RECOGNITION 11. SIGNAL PROCESSING 12. Signal, Image and Speech Processing 13. Sociophysics 14. Speech processing systems
Item TypeeBook
Multi-Media Links
Please Click here for eBook
Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
I09105     On Shelf    

5.     
No image available
TitleThe Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction
Author(s)Stark, Giordon
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXVI, 257 p. 150 illus., 140 illus. in color : online resource
Abstract NoteThis thesis represents one of the most comprehensive and in-depth studies of the use of Lorentz-boosted hadronic final state systems in the search for signals of Supersymmetry conducted to date at the Large Hadron Collider. A thorough assessment is performed of the observables that provide enhanced sensitivity to new physics signals otherwise hidden under an enormous background of top quark pairs produced by Standard Model processes. This is complemented by an ingenious analysis optimization procedure that allowed for extending the reach of this analysis by hundreds of GeV in mass of these hypothetical new particles. Lastly, the combination of both deep, thoughtful physics analysis with the development of high-speed electronics for identifying and selecting these same objects is not only unique, but also revolutionary. The Global Feature Extraction system that the author played a critical role in bringing to fruition represents the first dedicated hardware device for selecting these Lorentz-boosted hadronic systems in real-time using state-of-the-art processing chips and embedded systems
ISBN,Price9783030345488
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. Elementary particles (Physics) 4. Elementary Particles, Quantum Field Theory 5. MACHINE LEARNING 6. Measurement Science and Instrumentation 7. Measurement?????? 8. Particle acceleration 9. Particle Acceleration and Detection, Beam Physics 10. PHYSICAL MEASUREMENTS 11. QUANTUM FIELD THEORY
Item TypeeBook
Multi-Media Links
Please Click here for eBook
Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
I09004     On Shelf    

6.     
No image available
TitleThoughtful machine learning: A Test-Driven Aproach
Author(s)Matthew Kirk
PublicationMumbai, Shroff Publishers and Distributors Pvt. Ltd., 2014.
Descriptionxiv, 217p
ISBN,Price9789351108573 : 475.00(PB)
Classification681.3
Keyword(s)1. CLUSTERING 2. MACHINE LEARNING 3. NEURAL NETWORK 4. PROGRAMMING
Item TypeBook

Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
026224   681.3/KIRK/026224  On Shelf    

+Copy Specific Information
7.     
No image available
TitleFundamentals of deep learning: designing next generation machine intelligence algorithms
Author(s)Nikhil Buduma
PublicationO'Reilly Media
Descriptionxii,283p
ISBN,Price9789352135608 : 775.00
Classification681.3
Keyword(s)1. Deep Learning 2. Machine Intelligence Algorithms 3. MACHINE LEARNING 4. NEURAL NETWORKS 5. NKN PROJECT
Item TypeBook

Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
PP0025   681.3/BUD/PP0025  Issued NKN: NKN Project 31/Mar/2019

+Copy Specific Information
8.     
No image available
TitlePattern recognition and machine learning
Author(s)Christopher M. Bishop
PublicationNew York, Springer, 2011.
Descriptionxx, 738
ISBN,Price9780387310732 : Rs.3399
Classification621.397.3:681.3
Keyword(s)1. BIOINFORMATICS 2. COMPUTER SCIENCE 3. COMPUTER VISION 4. DATA MINING 5. MACHINE LEARNING 6. NKN PROJECT 7. SIGNAL PROCESSING 8. STATISTICS
Item TypeBook

Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
026213   621.397.3:681.3/BIS/026213  Issued PP07: Prasia P. 23/Jul/2022
PP0019   621.397.3:681.3/BIS/PP0019  Issued AK01: Kembhavi 12/Jun/2022

+Copy Specific Information
9.     
No image available
TitleDeep learning
Author(s)Ian Goodfellow;Yoshua Bengio;Aaron Courville
PublicationCambridge, MIT Press, 2017.
Descriptionxxii, p. 775
Series(Adaptive Computationa and Machine Learning)
Contents NoteDeep Learning website : www.deeplearningbook.org
ISBN,Price9780262035613 : US$ 80.00(HB)
Classification681.3
Keyword(s)1. Deep Learning 2. Deep Learning Research 3. Deep Networks 4. MACHINE LEARNING 5. NKN PROJECT
Item TypeBook

Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
026211   681.3/GOO/026211  Issued PC04: Chordia, Pravinkumar A. 16/Jul/2022
PP0018   681.3/GOO/PP0018  Issued AK01: Kembhavi 12/Jun/2022

+Copy Specific Information
10.    
No image available
TitlePattern recognition and machine learning
Author(s)Yuichiro Anzai
PublicationBoston, Academic Press, Inc., 1992.
Description407pp.
Classification621.397.3:681.3
Keyword(s)1. MACHINE LEARNING 2. PATTERN PERCEPTION
Item TypeBook

Circulation Data
Accession#  Call#StatusIssued ToReturn Due On Physical Location
008160   621.397.3:681.3/ANZ/008160  On Shelf    

+Copy Specific Information
(page:1 / 2) [#11]    Next Page   Last Page