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Interperetable AI

About Interperetable AI

AI models can become so complex that even experts have difficulty understanding themand forget the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. Youll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when youre facing a problem that demands the predictive power of ahard-to-interpret deep learning model.

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  • Language:
  • English
  • ISBN:
  • 9781617297649
  • Binding:
  • Paperback
  • Pages:
  • 275
  • Published:
  • October 16, 2022
  • Dimensions:
  • 235x188x21 mm.
  • Weight:
  • 612 g.
  In stock
Delivery: 3-5 business days
Expected delivery: December 7, 2024
Extended return policy to January 30, 2025

Description of Interperetable AI

AI models can become so complex that even experts have difficulty understanding themand forget the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. Youll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when youre facing a problem that demands the predictive power of ahard-to-interpret deep learning model.

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