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

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. 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. 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. 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. 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. 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. 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.