9781032288284 - mathematical engineering of deep learning di liquet, benoit; moka, sarat; nazarathy, yoni (19 risultati)
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: California Books, Miami, FL, U.S.A.California Books
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Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Condizione: New. In.
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. 1st edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Paperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Lingua: Inglese
Editore: CRC Press, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: moluna, Greven, Germaniamoluna
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Condizione: New.
Lingua: Inglese
Editore: Chapman & Hall, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 406 pages. 10.00x7.00x10.00 inches. In Stock.
Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condizione: new. Paperback. Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These i…deas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.Key Features:A perfect summary of deep learning not tied to any computer language, or computational framework.An ideal handbook of deep learning for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field. Provides a complete and concise overview of deep learning using the language of mathematics. Provides a self-contained background on machine learning and optimization algorithms, and progresses through the key ideas of deep learning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: CRC Press, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
- Brossura
- Print on Demand
Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Lingua: Inglese
Editore: CRC Press, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
- Brossura
- Print on Demand
Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
- Brossura
- Print on Demand
Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
- Brossura
- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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Paperback. Condizione: new. Paperback. Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These i…deas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.Key Features:A perfect summary of deep learning not tied to any computer language, or computational framework.An ideal handbook of deep learning for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field. Provides a complete and concise overview of deep learning using the language of mathematics. Provides a self-contained background on machine learning and optimization algorithms, and progresses through the key ideas of deep learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
- Brossura
- Print on Demand
Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Paperback. Condizione: new. Paperback. Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These i…deas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.Key Features:A perfect summary of deep learning not tied to any computer language, or computational framework.An ideal handbook of deep learning for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field. Provides a complete and concise overview of deep learning using the language of mathematics. Provides a self-contained background on machine learning and optimization algorithms, and progresses through the key ideas of deep learning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Chapman And Hall/CRC, 2024
Serie: Chapman & Hall/CRC Data Science, Libro 36 di 36. Libro 36 di 36 - Chapman & Hall/CRC Data Science
- Brossura
- Print on Demand
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 162,10
EUR 63,90 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Provides a complete and concise overview of deep learning using the language of mathematics. Provides a self-contained background on machine learning and optimization algorithms, and progresses through the key ideas of deep learning.
