ISBN: 9781098119133 - explainable ai for practitioners: designing and implementing explainable ml solutions (25 risultati)

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Paperback. Condizione: New. Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.…

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Paperback. Condizione: New. Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.…

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Paperback. Condizione: new. Paperback. 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 Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: Brand New. 350 pages. 9.19x7.00x0.80 inches. In Stock.

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Condizione: New. 2022. Paperback. . . . . .

Paperback. Condizione: New. Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.…

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Condizione: New. &Uumlber den AutorMichael Munn 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.

Taschenbuch. Condizione: Neu. Neuware - 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.…

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Paperback. Condizione: new. Paperback. 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 Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

Paperback. Condizione: New. Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.…