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Interpretable AI : = building explainable machine learning systems /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Interpretable AI :/ Ajay Thampi.
Reminder of title:	building explainable machine learning systems /
Author:	Thampi, Ajay,
Published:	Shelter Island, NY :Manning Publications, : c2022.,
Description:	xxi, 304 p. :ill. ; : 24 cm.;
Notes:	Includes index.
Subject:	Artificial intelligence. -
ISBN:	9781617297649 (pbk.) :

Interpretable AI : = building explainable machine learning systems /
Thampi, Ajay,

Interpretable AI :building explainable machine learning systems /Ajay Thampi. - Shelter Island, NY :Manning Publications,c2022. - xxi, 304 p. :ill. ;24 cm.

Includes index.

It's often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside "black box" models, designing accountable algorithms, and understanding the factors that cause skewed results. "Interpretable AI" teaches you to identify the patterns your model has learned and why it produces its results. As you read, you'll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You'll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python.--

ISBN: 9781617297649 (pbk.) :NT1924

LCCN: 2022289405Subjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q335 / .T468 2022

Dewey Class. No.: 006.3

Interpretable AI : = building explainable machine learning systems /
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245 1 0 $a Interpretable AI : $b building explainable machine learning systems / $c Ajay Thampi.
260 # $a Shelter Island, NY : $b Manning Publications, $c c2022.
300 $a xxi, 304 p. : $b ill. ; $c 24 cm.
500 $a Includes index.
520 # $a It's often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside "black box" models, designing accountable algorithms, and understanding the factors that cause skewed results. "Interpretable AI" teaches you to identify the patterns your model has learned and why it produces its results. As you read, you'll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You'll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python.-- $c Source other than the Library of Congress.
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650 # 0 $a Python (Computer program language) $3 566246
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