Books [Johan Hoffman, Claes Johnson] Computational Turbulent Incompressible Flow: Applied Mathematics: Body and Soul 4
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This book, Volume 4 of the book series of the Body and Soul mathematics education reform program, presents a unified new approach to computational simulation of turbulent flow starting from the general basis of calculus and linear algebra covered in Volumes 1 through 3. The book puts the Body and Soul computational finite element methodology in the form of General Galerkin (G2) up against the challenge of computing turbulent solutions of the inviscid Euler equations and the Navier-Stokes equations with small viscosity. It shows that direct application of G2 without any turbulence or wall modeling, allows reliable computation on a PC...
Books [Teiko Heinosaari, Mario Ziman] Guide to Mathematical Concepts of Quantum Theory
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Books [Avi Wigderson] Mathematics and Computation: A Theory Revolutionizing Technology and Science
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From the winner of the Turing Award and the Abel Prize, an introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy
Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific...
Books [Etienne E Kerre, John Mordeson] Fuzzy Mathematics
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This book provides a timely overview of topics in fuzzy mathematics. It lays the foundation for further research and applications in a broad range of areas. It contains break-through analysis on how results from the many variations and extensions of fuzzy set theory can be obtained from known results of traditional fuzzy set theory. The book contains not only theoretical results, but a wide range of applications in areas such as decision analysis, optimal allocation in possibilistics and mixed models, pattern classification, credibility measures, algorithms for modeling uncertain data, and numerical methods for solving fuzzy linear...
Books [Denny Burzynski] Mathematics for Game Developers
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Books [Ved Prakash Gupta, Prabha Mandayam, V. S. Sunder] The Functional Analysis of Quantum Information Theory
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This book provides readers with a concise introduction to current studies on operator-algebras and their generalizations, operator spaces and operator systems, with a special focus on their application in quantum information science. This basic framework for the mathematical formulation of quantum information can be traced back to the mathematical work of John von Neumann, one of the pioneers of operator algebras, which forms the underpinning of most current mathematical treatments of the quantum theory, besides being one of the most dynamic areas of twentieth century functional analysis. Today, von Neumann’s foresight finds...
Books [Jean Gallier, Jocelyn Quaintance] Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning
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This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer...
Books [Seongjai Kim] Mathematical Foundations of Machine Learning
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Books [Gerald Teschl] Mathematical Methods in Quantum Mechanics: With Applications to Schrodinger Operators
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Quantum mechanics and the theory of operators on Hilbert space have been deeply linked since their beginnings in the early twentieth century. States of a quantum system correspond to certain elements of the configuration space and observables correspond to certain operators on the space. This book is a brief, but self-contained, introduction to the mathematical methods of quantum mechanics, with a view towards applications to Schrodinger operators. Part 1 of the book is a concise introduction to the spectral theory of unbounded operators. Only those topics that will be needed for later applications are covered. The spectral theorem...
Books [Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong] Mathematics for Machine Learning
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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal...
Books [David Logan] Applied Mathematics, 4 edition
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Praise for the Third Edition
“Future mathematicians, scientists, and engineers should find the book to be an excellent introductory text for coursework or self-study as well as worth its shelf space for reference.”
―MAA Reviews
Applied Mathematics, Fourth Edition is a thoroughly updated and revised edition on the applications of modeling and analyzing natural, social, and technological processes. The book covers a wide range of key topics in mathematical methods and modeling and highlights the connections between mathematics and the applied and natural sciences.
The Fourth Edition covers both standard and modern topics, including...
Books [Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger] Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
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This book aims to provide an introduction to the topic of deep learning algorithms. We review essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures (such as fully-connected feedforward ANNs, convolutional ANNs, recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different optimization algorithms (such as the basic Stochastic Gradient Descent (SGD) method, accelerated methods, and adaptive methods).
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Books [Jeremy Pickles] Applied Mathematics by Example
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Mathematics is an exceptionally useful subject, having numerous applications in business, computing, engineering and medicine to name but a few. `Applied mathematics’ refers to the study of the physical world using mathematics.
This book approaches the subject from an oft-neglected historical perspective. A particular aim is to make accessible to students Newton’s vision of a single system of law governing the falling of an apple and the orbital motion of the moon.
The book and its associated volume of practice problems give an excellent introduction to applied mathematics.
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Books [Alan Parks] Introduction to Applied Mathematics, Fourth Edition
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An introduction to applied mathematics, including calculus, for students of the social and life sciences. Exact and numerical calculation on a variety of mathematical models. Recursion, equilibria, differentiation, integration, linear algebra, linear and non-linear optimization. Separate online problems document available.
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Books [Manas Joshi] The AI Expedition: From Basics to Brilliance
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Artificial intelligence is reshaping the technology, introducing new advancements and opportunities, guiding us into a future filled with extraordinary progress and possibilities. "The AI Expedition: From Basics to Brilliance" is your essential guide to navigating this fascinating world, offering a deep dive into the core algorithms that power AI, and unraveling the mysteries that lie at the heart of this revolutionary technology.
Designed to be a comprehensive yet accessible exploration of AI, this book takes readers on an enriching journey from the foundational concepts to the most advanced techniques. Whether you are a student, a...
Books [Thomas D. Grant, Damon J. Wischik] On the Path to AI: Conceptual Foundations of the Machine Learning Age
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This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age―prediction based on datasets.
On the path to AI introduces readers to the key concepts of machine learning, discusses...
Books [Bernd Carsten Stahl] Artificial Intelligence for a Better Future
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This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical...
Books [Ovidiu Vermesan] Artificial Intelligence for Digitising Industry – Applications
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This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI project, including an overview of industrial use cases, research, technological innovation, validation, and deployment.
This book’s sections build on the research, development, and innovative ideas elaborated for applications in...
Books [Herman Cappelen, Josh Dever] Making AI Intelligible: Philosophical Foundations
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Can humans and artificial intelligences share concepts and communicate? Making AI Intelligible shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved.
The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to...
Books [Lili Yan Ing, Gene M. Grossman] Robots and AI: A New Economic Era
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Robots and artificial intelligence (AI) are powerful forces that will likely have large impacts on the size, direction, and composition of international trade flows. This book discusses how industrial robots, automation, and AI affect international growth, trade, productivity, employment, wages, and welfare. The book explains new approaches on how robots and artificial intelligence affect the world economy by presenting detailed theoretical framework and country-specific as well as firm-product level-specific exercises.
This book will be a useful reference for those researching on robots, automation, AI and their economic impacts on...
Books [Huawei] Artificial Intelligence Technology
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This open access book aims to give our readers a basic outline of today's research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application.
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Books [Kresimir Delac, Mislav Grgic, Marian Stewart Bartlett] Recent Advances in Face Recognition
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The main idea and the driver of further research in the area of face recognition are security applications and human-computer interaction. Face recognition represents an intuitive and non-intrusive method of recognizing people and this is why it became one of three identification methods used in e-passports and a biometric of choice for many other security applications.
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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Books [Sue Curry Jansen] What Was Artificial Intelligence?
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When it was originally published in 2002, Sue Curry Jansen’s “What Was Artificial Intelligence?” attracted little notice. The long essay was published as a chapter in Jansen’s Critical Communication Theory, a book whose wisdom and erudition failed to register across the many fields it addressed. One explanation for the neglect, ironic and telling, is that Jansen’s sheer scope as an intellectual had few competent readers in the communication studies discipline into which she published the book. “What Was Artificial Intelligence?” was buried treasure. In this mediastudies.press edition, Jansen’s prescient autopsy of AI self-selling—the...
Books [Micael Dagraça] Practical Game AI Programming
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A smart and diverse game AI is considered to be one of the main pillars of a successful game. This book will help you to get inside game AI programming, implement the latest cutting edge algorithms with C#, and finally help you create effective and interesting AI for your game projects
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Books [Hal Daume III] A Course in Machine Learning
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This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
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