國立高雄大學圖資館 |

Language: English

Back

Explainable AI recipesimplement solutions to model explainability and interpretability with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable AI recipesby Pradeepta Mishra.
Reminder of title:	implement solutions to model explainability and interpretability with Python /
Author:	Mishra, Pradeepta.
Published:	Berkeley, CA :Apress :2023.
Description:	xxiv, 254 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-1-4842-9029-3
ISBN:	9781484290293$q(electronic bk.)

Explainable AI recipesimplement solutions to model explainability and interpretability with Python /
Mishra, Pradeepta.

Explainable AI recipesimplement solutions to model explainability and interpretability with Python /[electronic resource] :by Pradeepta Mishra. - Berkeley, CA :Apress :2023. - xxiv, 254 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Explainability Library Installations -- Chapter 2: Linear Supervised Model Explainability -- Chapter 3: Non-Linear Supervised Learning Model Explainability -- Chapter 4: Ensemble Model for Supervised Learning Explainability -- Chapter 5: Explainability for Natural Language Modeling -- Chapter 6: Time Series Model Explainability -- Chapter 7: Deep Neural Network Model Explainability.

Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.

ISBN: 9781484290293$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-9029-3doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .M57 2023

Dewey Class. No.: 006.3

Explainable AI recipesimplement solutions to model explainability and interpretability with Python /
LDR:02930nmm a2200325 a 4500 001 640646
003 DE-He213
005 20230301192708.0
006 m d
007 cr nn 008maaau
008 231025s2023 cau s 0 eng d
020 $a 9781484290293$q(electronic bk.)
020 $a 9781484290286$q(paper)
024 7 $a 10.1007/978-1-4842-9029-3 $2 doi
035 $a 978-1-4842-9029-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q335 $b .M57 2023
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q335 $b .M678 2023
100 1 $a Mishra, Pradeepta. $3 834026
245 1 0 $a Explainable AI recipes $h [electronic resource] : $b implement solutions to model explainability and interpretability with Python / $c by Pradeepta Mishra.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xxiv, 254 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to Explainability Library Installations -- Chapter 2: Linear Supervised Model Explainability -- Chapter 3: Non-Linear Supervised Learning Model Explainability -- Chapter 4: Ensemble Model for Supervised Learning Explainability -- Chapter 5: Explainability for Natural Language Modeling -- Chapter 6: Time Series Model Explainability -- Chapter 7: Deep Neural Network Model Explainability.
520 $a Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Python. $3 763308
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-9029-3
950 $a Professional and Applied Computing (SpringerNature-12059)