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Interpretable AI: how to make AI understandable

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Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting-edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and open source libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you to identify the patterns your model has learned, and presents best practices for building fair and unbiased models. When you’re done, you’ll be able to improve your AI’s performance during training, and build robust systems that counteract errors from bias, data leakage, and concept drift.

Learn more about the book here: https://www.manning.com/books/interpretable-ai

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Interpretable AI: how to make AI understandable

  1. 1. Transparent and understandable AI systems with Interpretable AI. Take 40% off purchase by entering slthampi into the discount code box at checkout at manning.com.
  2. 2. 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.
  3. 3. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. The book 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.
  4. 4. Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI. When you’re done, you’ll be able to improve your AI’s performance during training, and build robust systems that counteract errors from bias, data leakage, and concept drift.
  5. 5. What people are saying about the book: Techniques are consistently presented with excellent examples. -James J. Byleckie I think this is a valuable book both for beginners as well for more experienced users. -Kim Falk Jørgensen This book provides a great insight into the interpretability step of developing a structured learning robust AI systems. -Izhar Haq
  6. 6. About the author: Ajay Thampi is a machine learning engineer at a large tech company primarily focused on responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.
  7. 7. Take 40% off Interpretable AI by entering slthampi into the discount code box at checkout at manning.com. If you want to see more, you can preview the book’s contents on our browser-based liveBook reader here.

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