Erin Mills Town Centre Gift Cards are the perfect choice for your gift giving needs.Purchase gift cards at kiosks near the food court or centre court, at Guest Services, or click below to purchase online.PURCHASE HERE
Indigo Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines | Erin Mills Town Centre
*Product information may vary - to confirm product availability, pricing, shipping and return information please contact Indigo
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable.
Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world.
You'll learn:
Methods to explain ML models and their outputs to stakeholders
How to recognize and fix fairness concerns and privacy leaks in an ML pipeline
How to develop ML systems that are robust and secure against malicious attacks
Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable.
Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world.
You'll learn:
Methods to explain ML models and their outputs to stakeholders
How to recognize and fix fairness concerns and privacy leaks in an ML pipeline
How to develop ML systems that are robust and secure against malicious attacks
Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
The largest book retailer in Canada also offers toys, music, home décor, gifts and lifestyle products. What's Inside...Books, Magazines, CD’s and DVD’s, Toys and Gifts, Home Accents, Electronics, Baby’s and Children’s Section, Bath and Body, Kitchen and Bedroom, Stationary Located outside in the exterior plaza.
