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Explainable AI for Practitioners
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 needs
Tips and best practices for implementing these techniques
A guide to interacting with explainability and how to avoid common pitfalls
The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
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 needs
Tips and best practices for implementing these techniques
A guide to interacting with explainability and how to avoid common pitfalls
The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
- GenresArtificial Intelligence
278 pages, ebook
Published October 31, 2022
1 person is currently reading
20 people want to read
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Displaying 1 of 1 review
January 4, 2023
Great book on Explainable AI from the technical point of view is a good reference that will allow you to learn more about techniques for performing Explainability when implementing ML models and algorithms.
The authors go from basics to most profound levels by showing you examples and real use cases of what it is used in the industry and not only that but raise awareness that this is a new area and it’s full of opportunities for exploring, researching and implementing new techniques around the area, always keeping in mind that one have to adjust the levels depending of the audience who is receiving the explanations itself.
I truly enjoyed reading this book and learnt a lot by the content exposed in it.
The authors go from basics to most profound levels by showing you examples and real use cases of what it is used in the industry and not only that but raise awareness that this is a new area and it’s full of opportunities for exploring, researching and implementing new techniques around the area, always keeping in mind that one have to adjust the levels depending of the audience who is receiving the explanations itself.
I truly enjoyed reading this book and learnt a lot by the content exposed in it.
Displaying 1 of 1 review