★★★★☆
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2019-06-08
Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists - de Tobias Baer (Author)
Details Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists
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| Le Titre Du Fichier | Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists |
| Date de Parution | 2019-06-08 |
| Traducteur | Ruchi Senara |
| Chiffre de Pages | 853 Pages |
| Taille du fichier | 50.95 MB |
| Langue du Livre | Anglais & Français |
| Éditeur | Dun Emer Press |
| ISBN-10 | 8076180171-XXT |
| Format de Données | AMZ PDF ePub ASC OSHEET |
| de (Auteur) | Tobias Baer |
| Digital ISBN | 267-5632284257-DPY |
| Nom de Fichier | Understand-Manage-and-Prevent-Algorithmic-Bias-A-Guide-for-Business-Users-and-Data-Scientists.pdf |
Télécharger Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists Livre PDF Gratuit
Understand Manage and Prevent Algorithmic Bias by by Tobias Baer English 2019 ISBN 1484248848 EPUB True PDF 260 pages 1 Mb Algorithms The book helps you understand how to manage algorithmic bias and how data science can prevent bias from algorithms
Read this interdisciplinary book about algorithmic bias to understand where algorithmic bias comes from how to manage it as a business user or regulator and how data science can prevent bias from entering statistical algorithms
Understand Manage and Prevent Algorithmic Bias A Guide for Business Users and Data Scientists 1st ed Tobias Baer ISBN 9781484248843 Are algorithms friend or foe The human mind is evolutionarily designed to take shortcuts in order to survive We jump to conclusions because our brains want to keep us safe A majority of our biases work
The algorithms that power fintech may discriminate in ways that can be difficult to anticipate—and financial institutions can be held accountable even when all…
A guide to machine learning algorithms and their applications Do you know the difference between supervised and unsupervised learning How about the difference between decision trees and forests Or when to use a support vector algorithm Get all the answers here
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