AI models can become so complex that even experts have difficulty understanding them—and forget about explaining the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. You’ll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when you’re facing a problem that demands the predictive power of a hard-to-interpret deep learning model.
I think this is a valuable book both for beginners as well for more experienced users.
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