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Deep Learning Computational Problems by Santikellur Pranesh (34 results)
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US$ 72.76
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence, 1052)
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence)
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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security
Published by Springer Verlag, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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US$ 107.14
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Deep Learning for Computational Problems in Hardware Security
Published by Springer Verlag, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Deep Learning for Computational Problems in Hardware Security (Hardcover)
Published by Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
Hardcover. Condition: new. Hardcover. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Deep Learning for Computational Problems in Hardware Security (Paperback)
Published by Springer Verlag, Singapore, Singapore, 2023
ISBN 10: 9811940193 ISBN 13: 9789811940194
Language: English
Paperback. Condition: new. Paperback. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Deep Learning For Computational Problems
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Condition: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
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Deep Learning for Computational Problems in Hardware Security
Published by Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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US$ 160.06
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Add to basketHardback. Condition: New. 2023 ed. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.
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Condition: New. pp. 100.
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Deep Learning for Computational Problems in Hardware Security
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
US$ 126.95
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Add to basketCondition: New. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent modeling attacks on Physically Unclonable Functio.
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US$ 173.70
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Add to basketPaperback. Condition: Brand New. 97 pages. 9.25x6.10x0.21 inches. In Stock.
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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
Published by Springer Nature Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
US$ 82.20
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US$ 176.01
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Add to basketHardcover. Condition: Brand New. 97 pages. 9.25x6.10x0.51 inches. In Stock.
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Deep Learning for Computational Problems in Hardware Security (Hardcover)
Published by Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
US$ 191.40
Convert currencyUS$ 37.00 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: new. Hardcover. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Deep Learning for Computational Problems in Hardware Security
Published by Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Language: English
US$ 154.27
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Add to basketHardback. Condition: New. 2023 ed. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.
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Deep Learning for Computational Problems in Hardware Security (Paperback)
Published by Springer Verlag, Singapore, Singapore, 2023
ISBN 10: 9811940193 ISBN 13: 9789811940194
Language: English
US$ 211.19
Convert currencyUS$ 37.00 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Deep Learning for Computational Problems in Hardware Security
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811940193 ISBN 13: 9789811940194
Language: English
US$ 110.28
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent modeling attacks on Physically Unclonable Functio.
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US$ 135.86
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Add to basketHardcover. Condition: Brand New. 97 pages. 9.25x6.10x0.51 inches. In Stock. This item is printed on demand.
