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Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.This essential book provides:A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needsTips and best practices for implementing these techniquesA guide to interacting with explainability and how to avoid common pitfallsThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systemsAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text dataExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Have you ever seen a man open the car door for a woman and take her hand to assist her out? Maybe youve watched a man pull out a chair for a woman in a restaurant and wondered, why is he doing that? This book gives insight into the time-honored history of chivalrous actions. It demonstrates different actions that should be taught as expectations for a woman and man in creating a lifetime living of character.
Presenting an accessible introduction to the contemporary issues and challenges facing all those engaged in the further development of inter-faith relationships, dialogue and partnership between the world religions, Pitman argues that the future of world peace and prosperity depends on the outcome.
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