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Automated Machine Learning : Hyperparameter Optimization, Neural Architecture Search, and Algorithm Selection with Cloud Platforms by Adnan Masood, PhD is a nonfiction available to read on EtoBox.

What is Automated Machine Learning : Hyperparameter Optimization, Neural Architecture Search, and Algorithm Selection with Cloud Platforms about?

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key Features Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice Eliminate mundane tasks in data engineering and reduce human errors in machine learning models Find out how you can make machine learning accessible for all users to promote decentralized processes Book Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure

Who reads Automated Machine Learning : Hyperparameter Optimization, Neural Architecture Search, and Algorithm Selection with Cloud Platforms?

Author

Adnan Masood, PhD

Publisher

Packt Publishing, Limited; Packt Publishing

Published

2021

Language

EN

ISBN

9781789347500

Category

nonfiction

Subjects

Science, Computer Science, Business