000			02049 a2200193 4500
003			OSt
005			20240712153415.0
008			240712b |||||||| |||| 00| 0 eng d
020			_a9781484285466
040			_bEnglish
082			_a006.31/MIS
100			_aMishra, Pradeepta
245			_aPractical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
260			_bApress _c2024
300			_axviii, 344p
520			_aThis 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 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.
650			_2AI 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.
942			_cB _2ddc
999			_c696011 _d696011