Explainable AI recipes : implement solutions to model explainability and interpretability with Python / Pradeepta Mishra.
2023
Q335
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Unlimited
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Details
Title
Explainable AI recipes : implement solutions to model explainability and interpretability with Python / Pradeepta Mishra.
Author
Mishra, Pradeepta, author.
ISBN
9781484290293 (electronic bk.)
1484290291 (electronic bk.)
1484290283
9781484290286
1484290291 (electronic bk.)
1484290283
9781484290286
Published
[Place of publication not identified] : Apress, 2023.
Language
English
Description
1 online resource (253 pages) : illustrations (black and white, and colour).
Item Number
10.1007/978-1-4842-9029-3 doi
Call Number
Q335
Dewey Decimal Classification
006.3
Summary
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.
Note
Includes index.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Available in Other Form
EXPLAINABLE AI RECIPES.
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Table of Contents
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.
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.