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  2. Informationsteknologi: generelt
  3. Juridiske aspekter ved IT

Interperetable AI

Af: Ajay Thampi Engelsk Paperback

Interperetable AI

Af: Ajay Thampi Engelsk Paperback
Tjek vores konkurrenters priser

AI models can become so complex that even experts have difficulty understanding them—and forget about explaining the nuances of a cluster of novel algorithms to a business stakeholder! InterpretableAI is filled with cutting-edge techniques that will improve your understanding of how your AI models function.

InterpretableAI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and opensource libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you toidentify the patterns your model has learned, and presents best practices for building fair and unbiased models.

How deep learning models produce their results is often a complete mystery, even to their creators. These AI"black boxes" can hide unknown issues—including data leakage, the replication of human bias, and difficulties complying with legal requirements such as the EU''s "right to explanation." State-of-the-art interpretability techniques have been developed to understand even the most complex deep learning models, allowing humans to follow an AI''s methods and to better detect when it has made a mistake.

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AI models can become so complex that even experts have difficulty understanding them—and forget about explaining the nuances of a cluster of novel algorithms to a business stakeholder! InterpretableAI is filled with cutting-edge techniques that will improve your understanding of how your AI models function.

InterpretableAI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and opensource libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you toidentify the patterns your model has learned, and presents best practices for building fair and unbiased models.

How deep learning models produce their results is often a complete mystery, even to their creators. These AI"black boxes" can hide unknown issues—including data leakage, the replication of human bias, and difficulties complying with legal requirements such as the EU''s "right to explanation." State-of-the-art interpretability techniques have been developed to understand even the most complex deep learning models, allowing humans to follow an AI''s methods and to better detect when it has made a mistake.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 275
ISBN-13: 9781617297649
Indbinding: Paperback
Udgave:
ISBN-10: 161729764X
Kategori: Juridiske aspekter ved IT
Udg. Dato: 17 okt 2022
Længde: 21mm
Bredde: 235mm
Højde: 188mm
Forlag: Manning Publications
Oplagsdato: 17 okt 2022
Forfatter(e): Ajay Thampi
Forfatter(e) Ajay Thampi

Kategori Juridiske aspekter ved IT

ISBN-13 9781617297649

Sprog Engelsk

Indbinding Paperback

Sider 275

Udgave

Længde 21mm

Bredde 235mm

Højde 188mm

Udg. Dato 17 okt 2022

Oplagsdato 17 okt 2022

Forlag Manning Publications

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