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Edité par Springer Verlag, Singapore, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
EUR 63,22
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Edité par Springer Verlag, Singapore, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
EUR 67,81
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierEtat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Ajouter au panierEtat : New.
Edité par Springer Verlag, Singapore, SG, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : Rarewaves.com UK, London, Royaume-Uni
EUR 85,53
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierHardback. Etat : New. 2023 ed. Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
Edité par Springer Nature Singapore, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 74,92
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Ajouter au panierGebunden. Etat : New. Provides a comprehensive and thorough investigation on safety concerns regarding machine learningShows readers to identify vulnerabilities in machine learning models and to improve the models in the training processDemonstrates formal verif.
Edité par Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 76,88
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities.The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
Edité par Springer Nature Singapore, Springer Nature Singapore Apr 2023, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 74,89
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierBuch. Etat : Neu. Neuware -Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities.The book aims to improve readers¿ awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 85,66
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Ajouter au panierEtat : New.
Edité par Springer Verlag, Singapore, SG, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 90,93
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierHardback. Etat : New. 2023 ed. Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
EUR 114,55
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierHardcover. Etat : Brand New. 338 pages. 9.25x6.10x0.81 inches. In Stock.
Vendeur : Toscana Books, AUSTIN, TX, Etats-Unis
EUR 158,53
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Ajouter au panierHardcover. Etat : new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Edité par Springer Nature Singapore Apr 2023, 2023
ISBN 10 : 9811968136 ISBN 13 : 9789811968136
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 74,89
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities.The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills. 340 pp. Englisch.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 80,51
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Ajouter au panierHardcover. Etat : Brand New. 338 pages. 9.25x6.10x0.81 inches. In Stock. This item is printed on demand.