Case Studies In Foreign Language Placement: Practices And Possibilities

Please refer to the section BELOW (and NOT ABOVE) this line for the product details – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – Title:Case Studies In Foreign Language Placement: Practices And PossibilitiesISBN13:9780980045901ISBN10:0980045908Author:Hudson, Thom (Editor), Clark, Martyn (Editor)Description: Binding:Paperback, PaperbackPublisher:National Foreign Langauge Resource CenterPublication Date:2008-09-08Weight:0.88 lbsDimensions:0.46” H x 7.44” L x 9.69” WNumber of Pages:220Language:English

Bioengineering: Concepts And Applied Principles

Please refer to the section BELOW (and NOT ABOVE) this line for the product details – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – Title:Bioengineering: Concepts And Applied PrinciplesISBN13:9781641162777ISBN10:1641162775Author:Malcolm, Billy (Editor)Description: Binding:Hardcover, HardcoverPublisher:Callisto ReferencePublication Date:2020-09-15Weight:1.69 lbsDimensions:Number of Pages:205Language:English

Designing Machine Learning Systems – 9781098107963

Designing Machine Learning SystemsAn Iterative Process for Production-Ready Applications Author(s): Chip Huyen Format: Paperback Publisher: O’Reilly Media, United States Imprint: O’Reilly Media ISBN-13: 9781098107963, 978-1098107963 Synopsis Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they’re data dependent, with data varying wildly from one use case to the next. In this book, you’ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision–such as how to process and create training data, which features to use, how often to retrain models, and what to monitor–in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems