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, 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.
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
Find out the difference between cloud and operations support systems (OSS)
Implement AutoML in enterprise cloud to deploy 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, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
Описание: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.
Автор: Prakash Kolla Bhanu, Kanagachidambaresan G. R. Название: Programming with TensorFlow: Solution for Edge Computing Applications ISBN: 3030570797 ISBN-13(EAN): 9783030570798 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics.
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