E-Books →Explainable AI for Practitioners Designing and Implementing Explainable ML Solutions

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