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\nModel-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.\n
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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.
\n ascunde descrierea- Editură: Springer Nature Switzerland AG
- Cod:
- Anul publicării: 2021
- Limba: Engleză
- Legarea: Moale
- Număr de pagini: 202
- Lățimea ambalajului: 23.5 cm
- Înălțimea ambalajului: 15.5 cm
- Adâncimea ambalajului: 2.6 cm
- Greutatea ambalajului: 336 g
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