Explainable ai with python
Auteur :
Gianfagna, Leonida / Di Cecco, Antonio
Éditeur :
Springer Nature Switzerland AG
ISBN :
9783030686390
Date de publication :
29 avr. 2021
Dimensions :
23,5 x 15,5 cm
Langue :
Anglais
Pays d'origine :
Suisse
Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.†Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future.