Practical Explainable AI Using Python : Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks / Pradeepta Mishra
Publication year: 2022
: 978-1-4842-7158-2
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 will: 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
: Artificial Intelligence, Explainable Artificial Intelligence, Interpretable Artificial Intelligence, Python, Interpret the Black-Box Models, Model Biasness in neural networks, Model Reliability, Trusting Black-box models, Time Series Models, Natural Language Processing, Deep Neural Networks, Machine Learning, Computer Vision
