Interperetable ai
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Interpretable AI 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 open source libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you to identify the patterns your model has learned, and presents best practices for building fair and unbiased models. When you're done, you'll be able to improve your AI's performance during training, and build robust systems that counter act errors from bias, data leakage, and concept drift.
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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Taal: en
Uitvoering: Paperback
Oorspronkelijke releasedatum: 17 oktober 2022
Aantal pagina's: 275
Hoofdauteur: Ajay Thampi
Hoofduitgeverij: Manning Publications
Fabrikant Naam: Libri GmbH
Fabrikantgegevens: De overige informatie van de fabrikant is momenteel niet beschikbaar.
Product breedte: 189 mm
Product hoogte: 18 mm
Product lengte: 235 mm
Studieboek: Nee
Verpakking breedte: 189 mm
Verpakking hoogte: 18 mm
Verpakking lengte: 235 mm
Verpakkingsgewicht: 609 g
EAN: 9781617297649