{"product_id":"explainable-ai-for-practitioners","title":"Explainable AI for Practitioners","description":"\u003cp\u003eMost 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.  \u003c\/p\u003e\u003cp\u003eExplainability 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eThis essential book provides:\u003c\/b\u003e  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA 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 needs  \u003c\/li\u003e\n\u003cli\u003eTips and best practices for implementing these techniques  \u003c\/li\u003e\n\u003cli\u003eA guide to interacting with explainability and how to avoid common pitfalls  \u003c\/li\u003e\n\u003cli\u003eThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems  \u003c\/li\u003e\n\u003cli\u003eAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data  \u003c\/li\u003e\n\u003cli\u003eExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cspan style=\"color: rgb(34, 34, 34); font-size: 24px; font-weight: 700;\"\u003eAbout the Author\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eMichael Munn\u003c\/strong\u003e is a research software engineer at Google. His work focuses on better understanding the mathematical foundations of machine learning and how those insights can be used to improve machine learning models at Google. Previously, he worked in the Google Cloud Advanced Solutions Lab helping customers design, implement, and deploy machine learning models at scale. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDavid Pitman\u003c\/strong\u003e is a staff engineer working in Google Cloud on the AI Platform, where he leads the Explainable AI team. He's also a co-organizer of PuPPy, the largest Python group in the Pacific Northwest. David has a Masters of Engineering degree and a BS in computer science from MIT, where he previously served as a research scientist.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Shroff\/O'Reilly","offers":[{"title":"Default Title","offer_id":50506279649350,"sku":"9789355422439","price":990.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/51-Fj5GxuOL._SX386_BO1_204_203_200.jpg?v=1790325046","url":"https:\/\/cb-india.com\/products\/explainable-ai-for-practitioners","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}