Interpretable AI building explainable machine learning systems
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretab...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, New York :
Manning Publications
[2022]
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| Edición: | [First edition] |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009670616206719 |
| Sumario: | AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model. |
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| Notas: | Includes index. |
| Descripción Física: | 1 online resource (248 pages) |
| ISBN: | 9781638350422 |
