Author: Kevin Brewer
Publisher: Springer
ISBN: 3319299549
Category : Computers
Languages : en
Pages : 196
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Book Description
This book will help future scientists to become more intelligent users of computing technology in their practice of science. The content is suitable for introductory courses on the foundations of computing and the specific application of computers in different areas of science. The text presents a set of modules for use in existing science courses in order to integrate individual aspects of computational thinking, as well as a set of modules introducing the computer science concepts needed to understand the computing involved. These modules guide science students in their independent learning. The book covers computing applications in such diverse areas as bioinformatics, chemical kinetics, hydrogeological modeling, and mechanics of materials, geographic information systems, flow analysis, the solving of equations, curve fitting, optimization, and scientific data acquisition. The computing topics covered include simulations, errors, data representation, algorithms, XMS, compression, databases, performance, and complexity.
Author: Sergei Kurgalin
Publisher: Springer Nature
ISBN: 3030650529
Category : Computers
Languages : en
Pages : 122
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Book Description
This textbook is intended for practical, laboratory sessions associated with the course of quantum computing and quantum algorithms, as well as for self-study. It contains basic theoretical concepts and methods for solving basic types of problems and gives an overview of basic qubit operations, entangled states, quantum circuits, implementing functions, quantum Fourier transform, phase estimation, etc. The book serves as a basis for the application of new information technologies in education and corporate technical training: theoretical material and examples of practical problems, as well as exercises with, in most cases, detailed solutions, have relation to information technologies. A large number of detailed examples serve to better develop professional competencies in computer science.
Author: John Lawrence Nazareth
Publisher: Springer Nature
ISBN: 3031217624
Category : Computers
Languages : en
Pages : 79
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Book Description
Numerical Algorithmic Science and Engineering (NAS&E), or more compactly, Numerical Algorithmics, is the theoretical and empirical study and the practical implementation and application of algorithms for solving finite-dimensional problems of a numeric nature. The variables of such problems are either discrete-valued, or continuous over the reals, or, and as is often the case, a combination of the two, and they may or may not have an underlying network/graph structure. This re-emerging discipline of numerical algorithmics within computer science is the counterpart of the now well-established discipline of numerical analysis within mathematics, where the latter’s emphasis is on infinite-dimensional, continuous numerical problems and their finite-dimensional, continuous approximates. A discussion of the underlying rationale for numerical algorithmics, its foundational models of computation, its organizational details, and its role, in conjunction with numerical analysis, in support of the modern modus operandi of scientific computing, or computational science & engineering, is the primary focus of this short monograph. It comprises six chapters, each with its own bibliography. Chapters 2, 3 and 6 present the book’s primary content. Chapters 1, 4, and 5 are briefer, and they provide contextual material for the three primary chapters and smooth the transition between them. Mathematical formalism has been kept to a minimum, and, whenever possible, visual and verbal forms of presentation are employed and the discussion enlivened through the use of motivating quotations and illustrative examples. The reader is expected to have a working knowledge of the basics of computer science, an exposure to basic linear algebra and calculus (and perhaps some real analysis), and an understanding of elementary mathematical concepts such as convexity of sets and functions, networks and graphs, and so on. Although this book is not suitable for use as the principal textbook for a course on numerical algorithmics (NAS&E), it will be of value as a supplementary reference for a variety of courses. It can also serve as the primary text for a research seminar. And it can be recommended for self-study of the foundations and organization of NAS&E to graduate and advanced undergraduate students with sufficient mathematical maturity and a background in computing. When departments of computer science were first created within universities worldwide during the middle of the twentieth century, numerical analysis was an important part of the curriculum. Its role within the discipline of computer science has greatly diminished over time, if not vanished altogether, and specialists in that area are now to be found mainly within other fields, in particular, mathematics and the physical sciences. A central concern of this monograph is the regrettable, downward trajectory of numerical analysis within computer science and how it can be arrested and suitably reconstituted. Resorting to a biblical metaphor, numerical algorithmics (NAS&E) as envisioned herein is neither old wine in new bottles, nor new wine in old bottles, but rather this re-emerging discipline is a decantation of an age-old vintage that can hopefully find its proper place within the larger arena of computer science, and at what appears now to be an opportune time.
Author: Ragav Venkatesan
Publisher: CRC Press
ISBN: 1351650327
Category : Computers
Languages : en
Pages : 204
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Book Description
This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.
Author: Tracy Bowell
Publisher: Psychology Press
ISBN: 9780415240178
Category : Education
Languages : en
Pages : 294
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Book Description
A much-needed guide to thinking critically for oneself and how to tell a good argument from a bad one. Includes topical examples from politics, sport, medicine, music, chapter summaries, glossary and exercises.
Author: Gerard O'Regan
Publisher: Springer Nature
ISBN: 3030284948
Category : Computers
Languages : en
Pages : 309
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Book Description
This practically-focused textbook provides a concise and accessible introduction to the field of software testing, explaining the fundamental principles and offering guidance on applying the theory in an industrial environment. Topics and features: presents a brief history of software quality and its influential pioneers, as well as a discussion of the various software lifecycles used in software development; describes the fundamentals of testing in traditional software engineering, and the role that static testing plays in building quality into a product; explains the process of software test planning, test analysis and design, and test management; discusses test outsourcing, and test metrics and problem solving; reviews the tools available to support software testing activities, and the benefits of a software process improvement initiative; examines testing in the Agile world, and the verification of safety critical systems; considers the legal and ethical aspects of software testing, and the importance of software configuration management; provides key learning topics and review questions in every chapter, and supplies a helpful glossary at the end of the book. This easy-to-follow guide is an essential resource for undergraduate students of computer science seeking to learn about software testing, and how to build high quality and reliable software on time and on budget. The work will also be of interest to industrialists including software engineers, software testers, quality professionals and software managers, as well as the motivated general reader.
Author: Akira Maruoka
Publisher: Springer Science & Business Media
ISBN: 0857295357
Category : Computers
Languages : en
Pages : 285
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Book Description
This textbook presents a thorough foundation to the theory of computation. Combining intuitive descriptions and illustrations with rigorous arguments and detailed proofs for key topics, the logically structured discussion guides the reader through the core concepts of automata and languages, computability, and complexity of computation. Topics and features: presents a detailed introduction to the theory of computation, complete with concise explanations of the mathematical prerequisites; provides end-of-chapter problems with solutions, in addition to chapter-opening summaries and numerous examples and definitions throughout the text; draws upon the author’s extensive teaching experience and broad research interests; discusses finite automata, context-free languages, and pushdown automata; examines the concept, universality and limitations of the Turing machine; investigates computational complexity based on Turing machines and Boolean circuits, as well as the notion of NP-completeness.
Author: Gerard O'Regan
Publisher: Springer
ISBN: 3319640216
Category : Mathematics
Languages : en
Pages : 336
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Book Description
This invaluable textbook/reference provides an easy-to-read guide to the fundamentals of formal methods, highlighting the rich applications of formal methods across a diverse range of areas of computing. Topics and features: introduces the key concepts in software engineering, software reliability and dependability, formal methods, and discrete mathematics; presents a short history of logic, from Aristotle’s syllogistic logic and the logic of the Stoics, through Boole’s symbolic logic, to Frege’s work on predicate logic; covers propositional and predicate logic, as well as more advanced topics such as fuzzy logic, temporal logic, intuitionistic logic, undefined values, and the applications of logic to AI; examines the Z specification language, the Vienna Development Method (VDM) and Irish School of VDM, and the unified modelling language (UML); discusses Dijkstra’s calculus of weakest preconditions, Hoare’s axiomatic semantics of programming languages, and the classical approach of Parnas and his tabular expressions; provides coverage of automata theory, probability and statistics, model checking, and the nature of proof and theorem proving; reviews a selection of tools available to support the formal methodist, and considers the transfer of formal methods to industry; includes review questions and highlights key topics in every chapter, and supplies a helpful glossary at the end of the book. This stimulating guide provides a broad and accessible overview of formal methods for students of computer science and mathematics curious as to how formal methods are applied to the field of computing.
Author: Diane Cummings Persellin
Publisher: Taylor & Francis
ISBN: 1000976750
Category : Education
Languages : en
Pages : 125
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Book Description
This concise guidebook is intended for faculty who are interested in engaging their students and developing deep and lasting learning, but do not have the time to immerse themselves in the scholarship of teaching and learning. Acknowledging the growing body of peer-reviewed literature on practices that can dramatically impact teaching, this intentionally brief book:* Summarizes recent research on six of the most compelling principles in learning and teaching* Describes their application to the college classroom* Presents teaching strategies that are based on pragmatic practices* Provides annotated bibliographies and important citations for faculty who want to explore these topics further This guidebook begins with an overview of how we learn, covering such topics such as the distinction between expert and novice learners, memory, prior learning, and metacognition. The body of the book is divided into three main sections each of which includes teaching principles, applications, and related strategies – most of which can be implemented without extensive preparation.The applications sections present examples of practice across a diverse range of disciplines including the sciences, humanities, arts, and pre-professional programs. This book provides a foundation for the reader explore these approaches and methods in his or her teaching.
Author: Davide Pastorello
Publisher: Springer Nature
ISBN: 9811968977
Category : Computers
Languages : en
Pages : 144
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Book Description
This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research. To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.
