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Links to Free Computer, Mathematics, Technical Books all over the World
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- Title: Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
- Authors Lila Lee-Morrison
- Publisher: transcript publishing; 1st edition (December 27, 2019); eBook (Creative Commons Licensed)
- License(s): Attribution 3.0 Unported (CC BY 3.0)
- Hardcover: 198 pages
- eBook: PDF
- Language: English
- ISBN-10: 383764846X
- ISBN-13: 978-3837648461
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This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR.
About the Authors- N/A
- Computer Vision, Machine Vision, and Image Processing
- Deep Learning and Neural Networks
- Machine Learning
- Artificial Intelligence, Machine Learning, and Logic Programming
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- Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
- The Mirror Site (1) - PDF
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Pattern Recognition and Machine Learning (Christopher Bishop)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
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Graphs for Pattern Recognition: Systems of Linear Inequalities
This book deals with mathematical constructions that are foundational in such an important area of Data Mining as Pattern Recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities.
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Handbook of Digital Face Manipulation and Detection
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the both biometrics and media forensics fields.
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Portraits of Automated Facial Recognition (Lila Lee-Morrison)
Automated facial recognition algorithms are increasingly intervening in society. This book offers a unique analysis of these algorithms from a critical visual culture studies perspective.
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Recent Advances in Face Recognition (Kresimir Delac, et al)
This goal of this book is to provide the reader with the most up to date research performed in automatic face recognition. The chapters presented use innovative approaches to deal with a wide variety of unsolved issues.
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Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.
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Dive into Deep Learning (Aston Zhang, et al.)
This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.
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Machine Learning from Scratch (Danny Friedman)
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.
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Reinforcement Learning: An Introduction, Second Edition
It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.
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Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
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