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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 require...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Thampi, Ajay (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Shelter Island, NY : Manning Publications Co., [2022]
Edición:[First edition].
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
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.
Notas:Includes index.
Descripción Física:1 online resource (328 pages) : illustrations
ISBN:9781617297649
161729764X
9781638350422
1638350426