Practical Explainable AI Using Python, 1st ed. Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
- 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
Date de parution : 12-2021
Ouvrage de 344 p.
17.8x25.4 cm
Thème de Practical Explainable AI Using Python :
Mots-clés :
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