Robust Machine Learning de Guerraoui, Rachid Format Relié

Robust Machine Learning - Guerraoui, Rachid

Format: Relié

Note : 0

Soyez le premier à donner un avis

Vous en avez un à vendre ?

Vendez-le-vôtre

Filtrer par :

157,06 €

Produit Neuf

Ou 39,27 € /mois

Livraison à 0,01 €
Livré entre le 1 et le 9 juin

Voir les modes de livraison

PRO Vendeur favori

4,9/5 sur + de 1 000 ventes

Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9789819706877_dbm

Voir le détail de l'annonce

Nos autres offres

178,52 €

Produit Neuf

Ou 44,63 € /mois

En savoir plus
- Livraison à 0,01 €
Voir les modes de livraison
rarewaves-uk

PRO

4,8/5 sur + de 1 000 ventes

Afficher la description de l'annonce

Expédition rapide et soignée depuis l`Angleterre - Délai de livraison: entre 10 et 20 jours ouvrés.

Voir le détail de l'annonce

Vous avez choisi le retrait chez le vendeur à

Payez directement sur Rakuten (CB, PayPal, 4xCB...)
Récupérez le produit directement chez le vendeur
Rakuten vous rembourse en cas de problème

Terminer ma commande

Gratuit et sans engagement

Félicitations !

Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

TROUVER UN MAGASIN

Adresse

Retour

Horaires

Note :

Avis sur Robust Machine Learning de Guerraoui, Rachid Format Relié - Livre Informatique

Note : 0 0 avis sur Robust Machine Learning de Guerraoui, Rachid Format Relié - Livre Informatique

Donnez votre avis

Donnez votre avis sur ce produit

Votre note * : 1/5 2/5 3/5 4/5 5/5 Veuillez donner une note au produit

Le titre de votre avis* : Saisissez un titre

Quelques conseils :

Donnez le ton de votre avis, résumez-le en quelques mots qui apparaîtront comme le titre de votre avis.

Votre avis* : (40 caractères minimum) Saisissez un avis

Quelques conseils :

N'hésitez pas à mettre en avant les caractéristiques de ce produit, n'oubliez pas de mentionner ses avantages et/ou ses défauts éventuels. Décrivez précisément, argumentez, soyez convaincant !

Annuler

Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.

Produits similaires

Présentation Robust Machine Learning de Guerraoui, Rachid Format Relié
- Livre Informatique

Livre Informatique - Guerraoui, Rachid - 01/04/2024 - Relié - Langue : Anglais

Auteur(s) : Guerraoui, Rachid - Pinot, Rafael - Gupta, Nirupam

Editeur : Springer Singapore

Langue : Anglais

Parution : 01/04/2024

Format : Moyen, de 350g à 1kg

Nombre de pages : 188

Expédition : 453

Dimensions : 24.1 x 16.0 x 1.6

ISBN : 9789819706877

Résumé :
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area. ...

Biographie:

Rachid Guerraoui is a professor of computer science at EPFL, where he leads the Distributed Computing Laboratory. He has previously worked at the Ecole des Mines de Paris, CEA Saclay, HP Labs in Palo Alto, and MIT. ACM fellow and professor of the College de France, he was awarded a Senior ERC Grant and a Google Focused Award. He has co-authored several popular books on distributed computing, including Reliable and Secure Distributed Programming, and Algorithms for Concurrent Systems. Nirupam Gupta is a computer science research associate at EPFL. He has previously worked as a postdoc in the department of computer science at Georgetown University. He has served on the program committees of the dependable and secure machine learning workshops at the IEEE DSN conference and the symposium on reliable distributed systems (SRDS), and currently serves as a reviewer for leading control systems and optimization journals, including Elsevier Automatica, IEEE TAC and IEEE CONES. He received his PhD from the University of Maryland College Park, and his bachelor's degree from the Indian Institute of Technology Delhi. Rafael Pinot is a junior professor in the department of mathematics at Sorbonne Universit?, where he holds a chair on the mathematical foundation of computer and data science within the LPSM research unit. He previously worked as a computer science research associate at EPFL and received his PhD from PSL Research University. In 2018, he was awarded a JSPS summer fellowship to join Kyoto University as a visiting researcher. He also received the Dauphine Foundation's Young Researcher Award (2020) and the Postdoctoral Research Award from EPFL's Ecocloud Research Center (2021).

...

Sommaire:
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area. ...

Détails de conformité du produit

Consulter les détails de conformité de ce produit (

Personne responsable dans l'UE

)

Robust Machine Learning - Guerraoui, Rachid

Avis sur Robust Machine Learning de Guerraoui, Rachid Format Relié - Livre Informatique

Présentation Robust Machine Learning de Guerraoui, Rachid Format Relié - Livre Informatique

Présentation Robust Machine Learning de Guerraoui, Rachid Format Relié
- Livre Informatique