Название: Machine Learning Design Pattern (Early Release) Автор: Valliappa Lakshmanan, Sara Robinson, and Michael Munn Издательство: O’Reilly Media, Inc Год: 2020-07-21 Формат: epub/mobi/pdf(conv.) Размер: 10.8 Mb, 10 Mb Язык: English
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation. You’ll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure that models are treating users fairly
Dive Into Design Patterns Название: Dive Into Design Patterns Автор: Alexander Shvets Издательство: Independently published Год: 2018 Страниц: 410 Язык: английский Формат:...
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Machine Learning Design Pattern (Early Release) 
