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Automated Machine Learning: Hyperparameter optimization, neural architecture search a
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[Image: kejghwv-Vthn-CBHvwdj-Yqhv-Spog-Bsc5dg.jpg]

Automated Machine Learning: Hyperparameter optimization, neural architecture search and algorithm selection |  English | 2021 | ISBN-13 : 978-1800567689 | 312 Pages | True (PDF, EPUB, MOBI) | 168.77 MB


Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning.

Key Features

Get up to speed with AutoML using the platform of your choice, such as OSS, Azure, AWS, or GCP
Eliminate mundane tasks in data engineering and reduce human errors in ML models that occur mainly due to manual steps
Make machine learning accessible for all users, helping promote a decentralized process

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 more. You’ll explore different ways of implementing these techniques in open-source tools. Next, you’ll focus on enterprise tools, learning different ways of implementing AutoML in three major cloud service providers, including Microsoft Azure, Amazon Web Services (AWS), and the Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. Later chapters will show you how to develop accurate models by automating time-consuming and repetitive tasks involved in the machine learning development lifecycle.

By the end of this book, you’ll be able to build and deploy automated machine learning models that are not only accurate, but also increase productivity, allow interoperability, and minimize featuring engineering tasks.
What you will learn

Explore AutoML fundamentals, underlying methods, and techniques
Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario and differentiate between cloud and OSS offerings
Implement AutoML in tools such as AWS, Azure, and GCP and while deploying ML models and pipelines
Build explainable AutoML pipelines with transparency
Understand automated feature engineering and time series forecasting
Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems

Who This Book Is For

Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open-source tools, Microsoft Azure Machine Learning, Amazon Web Services (AWS), and Google Cloud Platform will find this book useful.

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