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Hardcover. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Essential Math for AI: Exploring Linear Algebra, Probability and Statistics, Calculus, Graph Theory, Discrete Mathematics, Numerical Methods, Optimization Techniques, and More (AI Fundamentals)
Book 2 of 5: AI FundamentalsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 14.91
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Paperback. Condition: Used; Very Good. **SHIPPED FROM UK** We believe you will be completely satisfied with our quick and reliable service. All orders are dispatched as swiftly as possible! Buy with confidence! Greener Books.
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Essential Math for AI: Exploring Linear Algebra, Probability and Statistics, Calculus, Graph Theory, Discrete Mathematics, Numerical Methods, Optimization Techniques, and More (AI Fundamentals)
Book 2 of 5: AI FundamentalsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Hardcover. Condition: UsedVery Good. Hardcover, a selected reprint volume; light fading, light shelf wear to exterior; otherwise contents in very good condition with clean text, firm binding.
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Condition: Sehr gut. A Selected Reprint Volume. XI, 724 Seiten, Zust: Gutes Exemplar. Schneller Versand und persönlicher Service - jedes Buch händisch geprüft und beschrieben - aus unserem Familienbetrieb seit über 25 Jahren. Eine Rechnung mit ausgewiesener Mehrwertsteuer liegt jeder unserer Lieferungen bei. Wir versenden mit der deutschen Post. Sprache: Englisch Gewicht in Gramm: 2030 gebundene Ausgabe gebundene Ausgabe.
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hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Numerical Linear Algebra Techniques For Systems and Control
Language: English
Published by IEEE Operations Center, 1994
ISBN 10: 0780304438 ISBN 13: 9780780304437
Condition: Used. pp. 736.
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Numerical Linear Algebra Techniques For Systems and Control
Language: English
Published by IEEE Operations Center, 1994
ISBN 10: 0780304438 ISBN 13: 9780780304437
Condition: Used. pp. 736.
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Numerical Linear Algebra Techniques For Systems and Control
Language: English
Published by IEEE Operations Center, 1994
ISBN 10: 0780304438 ISBN 13: 9780780304437
Condition: Used. pp. 736.
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Improving Performance of Sparse Numerical Linear Algebra Computations | Algorithmic optimization techniques for sparse direct and sparse iterative numerical solvers of large linear equations
Language: English
Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838334698 ISBN 13: 9783838334691
Taschenbuch. Condition: Neu. Improving Performance of Sparse Numerical Linear Algebra Computations | Algorithmic optimization techniques for sparse direct and sparse iterative numerical solvers of large linear equations | Piotr Luszczek | Taschenbuch | 84 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838334691 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Condition: Gut. Zustand: Gut | Seiten: 764 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Improving Performance of Sparse Numerical Linear Algebra Computations: Algorithmic optimization techniques for sparse direct and sparse iterative numerical solvers of large linear equations
Language: English
Published by LAP Lambert Academic Publishing, 2010
ISBN 10: 3838334698 ISBN 13: 9783838334691
Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Essential Math for AI: Exploring Linear Algebra, Probability and Statistics, Calculus, Graph Theory, Discrete Mathematics, Numerical Methods, Optimization Techniques, and More
Book 2 of 5: AI FundamentalsSeller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Essential Math for AI: Exploring Linear Algebra, Probability and Statistics, Calculus, Graph Theory, Discrete Mathematics, Numerical Methods, Optimization Techniques, and More
Book 2 of 5: AI FundamentalsSeller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 25.52
£ 14.56 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Are you ready to unlock the mathematical secrets that power today's most advanced artificial intelligence systems 'Essential Math for AI' is an essential guide for anyone looking to understand the complex mathematical underpinnings of AI. Whether you're an AI enthusiast, a student, or a professional in the field, this book is tailored to enrich your knowledge and prepare you for the future of AI innovation.Here's what you'll discover inside:Linear Algebra: Dive into the core of machine learning with in-depth explorations of vectors, matrices, and data transformations.Probability and Statistics: Learn how to make sense of data and uncertainty, which is crucial for developing robust AI applications.Calculus: Optimize AI models using the power of derivatives, integrals, and multivariable optimization.Graph Theory: Model complex relationships and understand the algorithms that can navigate these structures in AI.Discrete Mathematics: Tackle combinatorial problems and optimize algorithmic efficiency, a cornerstone of AI development.Numerical Methods: Solve equations and approximate functions, enhancing the computational power of AI.Optimization Techniques: From gradient descent to swarm intelligence, master the methods that enhance AI performance.Game Theory: Analyze strategic decision-making and its profound implications in AI.Information Theory: Quantify and encode data, ensuring efficiency and integrity in AI systems.Topology and Geometry: Uncover hidden structures in data, paving the way for breakthroughs in AI research.'Essential Math for AI' provides a comprehensive overview of the mathematical concepts propelling AI forward and offers a glimpse into the future of how these disciplines will continue to shape the AI landscape. With chapter summaries to consolidate your learning and a clear path charted for future exploration, this book is your roadmap to becoming well-versed in the mathematics of AI.Take the next step in your AI journey. Embrace the mathematical challenges and opportunities with 'Essential Math for AI.'ż.
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Taschenbuch. Condition: Neu. Essential Math for AI | Exploring Linear Algebra, Probability and Statistics, Calculus, Graph Theory, Discrete Mathematics, Numerical Methods, Optimization Techniques, and More | Andrew Hinton | Taschenbuch | Englisch | 2023 | Book Bound Studios | EAN 9781923045866 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Are you ready to unlock the mathematical secrets that power today's most advanced artificial intelligence systems 'Essential Math for AI' is an essential guide for anyone looking to understand the complex mathematical underpinnings of AI. Whether you're an AI enthusiast, a student, or a professional in the field, this book is tailored to enrich your knowledge and prepare you for the future of AI innovation.Here's what you'll discover inside:Linear Algebra: Dive into the core of machine learning with in-depth explorations of vectors, matrices, and data transformations.Probability and Statistics: Learn how to make sense of data and uncertainty, which is crucial for developing robust AI applications.Calculus: Optimize AI models using the power of derivatives, integrals, and multivariable optimization.Graph Theory: Model complex relationships and understand the algorithms that can navigate these structures in AI.Discrete Mathematics: Tackle combinatorial problems and optimize algorithmic efficiency, a cornerstone of AI development.Numerical Methods: Solve equations and approximate functions, enhancing the computational power of AI.Optimization Techniques: From gradient descent to swarm intelligence, master the methods that enhance AI performance.Game Theory: Analyze strategic decision-making and its profound implications in AI.Information Theory: Quantify and encode data, ensuring efficiency and integrity in AI systems.Topology and Geometry: Uncover hidden structures in data, paving the way for breakthroughs in AI research.'Essential Math for AI' provides a comprehensive overview of the mathematical concepts propelling AI forward and offers a glimpse into the future of how these disciplines will continue to shape the AI landscape. With chapter summaries to consolidate your learning and a clear path charted for future exploration, this book is your roadmap to becoming well-versed in the mathematics of AI.Take the next step in your AI journey. Embrace the mathematical challenges and opportunities with 'Essential Math for AI.'ż.
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Buch. Condition: Neu. Essential Math for AI | Exploring Linear Algebra, Probability and Statistics, Calculus, Graph Theory, Discrete Mathematics, Numerical Methods, Optimization Techniques, and More | Andrew Hinton | Buch | Englisch | 2023 | Book Bound Studios | EAN 9781923045873 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Improving Performance of Sparse Numerical Linear Algebra Computations : Algorithmic optimization techniques for sparse direct and sparse iterative numerical solvers of large linear equations
Language: English
Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838334698 ISBN 13: 9783838334691
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With new processor families appearing every few years, it is increasingly harder to achieve high performance rates in sparse matrix computations. This monograph studies new methods for sparse matrix factorizations and applies them efficiently while retaining ease of use of existing solutions. The implementations are timed and analyzed using a commonly accepted set of test matrices. Contemporary processors are used for the tests. The new factorization techniques are proven to be quite competitive with state of the art software. In addition, an optimization effort is applied to an iterative algorithm that stands out for its numerical robustness. This also gives satisfactory results on the tested computing platforms in terms of performance improvement. The same set of test matrices is used to enable an easy comparison between both investigated techniques, even though they are customarily treated separately in the literature. Possible extensions of the presented work range from easily conceivable merging with existing solutions to rather more evolved schemes dependent on hard to predict progress in theoretical and algorithmic research.
