Practical Explainable AI Using Python
Apress (Verlag)
978-1-4842-7157-5 (ISBN)
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, youwill 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.
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.
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.
| Erscheinungsdatum | 17.12.2021 |
|---|---|
| Zusatzinfo | 144 Illustrations, color; 50 Illustrations, black and white; XVIII, 344 p. 194 illus., 144 illus. in color. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | computer vision • deep neural networks • Explainable Artificial Intelligence • Interpretable Artificial Intelligence • Interpret the Black-Box Models • machine learning • Model Biasness in neural networks • model reliability • Natural Language Processing • Python • time series models • Trusting Black-box models |
| ISBN-10 | 1-4842-7157-2 / 1484271572 |
| ISBN-13 | 978-1-4842-7157-5 / 9781484271575 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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