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  • Format: ePub

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
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Produktbeschreibung
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, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.
By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.


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Autorenporträt
Adnan Masood, PhD is a visionary leader practitioner in the field of artificial intelligence, with over 20 years of experience in financial technology and large-scale systems development. He drives the firm's digital transformation, machine learning, and AI strategy. Dr. Masood collaborates with renowned institutions like Stanford AI Lab and MIT CSAIL, holds several patents in AI and machine learning, and is recognized by Microsoft as an AI MVP and Regional Director. In addition to his work in the technology industry, Dr. Masood is a published author, international speaker, STEM robotics coach, and diversity advocate.