Practical Explainable AI Using Python

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Beschreibung

Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.

You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision

Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing¿related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.

What You'll Learn

Review the different ways of making an AI model interpretable and explainable
Examine the biasness and good ethical practices of AI models
Quantify, visualize, and estimate reliability of AI models
Design frameworks to unbox the black-box models
Assess the fairness of AI models

Understand the building blocks of trust in AI models

Increase the level of AI adoption

Who This Book Is For

AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.

What You'll Learn

Who This Book Is For

AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.

Über den Autor

Pradeepta Mishra is the Head of AI (Leni) at L&T Infotech (LTI), leading a large group of data scientists, computational linguistics experts, machine learning and deep learning experts in building next generation product, 'Leni' world's first virtual data scientist. He was awarded as "India's Top - 40Under40DataScientists" by Analytics India Magazine. He is an author of 4 books, his first book has been recommended in HSLS center at the University of Pittsburgh, PA, USA. His latest book #PytorchRecipes was published by Apress. He has delivered a keynote session at the Global Data Science conference 2018, USA. He has delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions and community arranged forums.

Zusammenfassung

Covers the core features of explainability and how to execute them using Python frameworks

Explains XAI features to interpret supervised learning algorithms, NLP components and deep learning neural networks

Covers biasness, ethics and reliability description of AI algorithms and models.

Inhaltsverzeichnis

Chapter 1: Introduction to Model Explainability and Interpretability.- Chapter 2: AI Ethics, Biasness and Reliability.- Chapter 3: Model Explainability for Linear Models Using XAI Components.- Chapter 4: Model Explainability for Non-Linear Models using XAI Components.- Chapter 5: Model Explainability for Ensemble Models Using XAI Components.- Chapter 6: Model Explainability for Time Series Models using XAI Components.- Chapter 7: Model Explainability for Natural Language Processing using XAI Components.- Chapter 8: AI Model Fairness Using What-If Scenario.- Chapter 9: Model Explainability for Deep Neural Network Models.- Chapter 10: Counterfactual Explanations for XAI models.- Chapter 11: Contrastive Explanation for Machine Learning.- Chapter 12: Model-Agnostic Explanations By Identifying Prediction Invariance.- Chapter 13: Model Explainability for Rule based Expert System.- Chapter 14: Model Explainability for Computer Vision.

Details

Erscheinungsjahr:	2021
Fachbereich:	Programmiersprachen
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Seiten:	344
Inhalt:	xviii 344 S. 50 s/w Illustr. 144 farbige Illustr. 344 p. 194 illus. 144 illus. in color.
ISBN-13:	9781484271575
ISBN-10:	1484271572
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Mishra, Pradeepta
Hersteller:	APRESS
Maße:	254 x 178 x 19 mm
Von/Mit:	Pradeepta Mishra
Erscheinungsdatum:	15.12.2021
Gewicht:	0,684 kg

preigu-id: 120058087