Implement Solutions to Model Explainability and Interpretability with Python
Focusing on the practical application of Explainable AI (XAI), this book guides readers through using XAI libraries to enhance trust in AI and machine learning models. By employing a problem-solution framework, it effectively clarifies complex machine learning concepts and algorithms, making them accessible for practitioners looking to implement transparent AI solutions.
A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
The second edition offers updated code snippets for building neural network models with PyTorch, enhancing the learning experience. It introduces new chapters that delve into distributed modeling and deploying models in production, ensuring readers are equipped with the latest advancements in PyTorch. This comprehensive guide is designed for both beginners and experienced users looking to deepen their understanding of practical applications in machine learning.
Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks
Focusing on the intricacies of AI decision-making, this book delves into biases and the reliability of algorithms, particularly within black-box models. It emphasizes enhancing adaptability, interpretability, and explainability of AI outputs. Readers will engage with practical frameworks, utilizing Python XAI libraries, TensorFlow 2.0+, Keras, and custom Python wrappers to better understand and improve AI predictions.