Covers linear, non-linear, and time series models for comprehensive AI interpretation
Integrates popular XAI libraries and frameworks such as TensorFlow 2.0, Keras, LIME, SHAP, and more for hands-on learning
Explores both structured and unstructured data, including natural language processing applications
Focuses on ethical AI, bias detection, and reliability quantification to build trustworthy models
Includes counterfactual explanations and fairness assessment techniques for advanced model understanding
Summarized by Shop
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+, K