Practical Explainable AI Using Python

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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.