Menu

mon compte Connexion

mon panier 0 article

Vous avez entre 15 et 18 ans ? Profitez du Pass Culture à la librairie Eyrolles !
Tous nos rayons
Aucun résultat pour ""

Rayons

Graphisme & Photo Informatique Construction Entreprise & Droit Sciences Littérature Arts & Loisirs Vie pratique Voyage et Tourisme BD et Jeunesse

Déjà client ? Identifiez-vous

Mot de passe oublié ?

Nouveau client ?

CRÉER VOTRE COMPTE
Practical Fairness: Achieving Fair and Secure Data Models
Ajouter à une liste

Librairie Eyrolles - Paris 5e
Indisponible

Practical Fairness: Achieving Fair and Secure Data Models

Practical Fairness: Achieving Fair and Secure Data Models

Aileen Nielsen

(0 avis) Donner votre avis
175 pages, parution le 17/12/2020
Ajouter à une liste
Indisponible

Résumé

Fairness is becoming a paramount consideration for data scientists. This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias. Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms. Identify potential bias and discrimination in data science models Use preventive measures to minimize bias when developing data modeling pipelines Understand what data pipeline components implicate security and privacy concerns Write data processing and modeling code that implements best practices for fairness Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models Apply normative and legal concepts relevant to evaluating the fairness of machine learning models

Caractéristiques techniques

  PAPIER
Éditeur(s) O'Reilly
Auteur(s) Aileen Nielsen
Parution 17/12/2020
Nb. de pages 175
EAN13 9781492075738

Avantages Eyrolles.com

Livraison à partir de 0,01 € en France métropolitaine
Paiement en ligne SÉCURISÉ
Livraison dans le monde
Retour sous 15 jours
+ d'un million et demi de livres disponibles

Consultez aussi

satisfait ou remboursé
Satisfait ou remboursé
Paiement sécurisé
modes de paiement
Paiement à l'expédition
partout dans le monde
Livraison partout dans le monde
Service clients sav@commande.eyrolles.com
librairie française
Librairie française depuis 1925
Recevez nos newsletters
Vous serez régulièrement informé(e) de toutes nos actualités.
Inscription