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210823 ||| eng |
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|a 9781800565524
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| 050 |
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4 |
|a Q325.5
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| 100 |
1 |
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|a MASOOD, DR. ADNAN.
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| 245 |
0 |
0 |
|a AUTOMATED MACHINE LEARNING
|h [electronic resource]
|b hyperparameter optimization, neural architecture search, and... algorithm selection with cloud platforms
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| 260 |
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|a [S.l.]
|b PACKT PUBLISHING LIMITED
|c 2021
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| 300 |
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|a 1 online resource
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| 505 |
0 |
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|a Table of Contents A Lap around Automated Machine Learning Automated Machine Learning, Algorithms, and Techniques Automated Machine Learning with Open Source Tools and Libraries Getting Started with Azure Machine Learning Automated Machine Learning with Microsoft Azure Machine Learning with Amazon Web Services Doing Automated Machine Learning with Amazon SageMaker Autopilot Machine Learning with Google Cloud Platform Automated Machine Learning with GCP Cloud AutoML AutoML in the Enterprise
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 015 |
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|a GBC136522
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| 776 |
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|z 1800567685
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| 776 |
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|z 9781800567689
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| 776 |
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|z 1800565526
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| 776 |
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|z 9781800565524
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781800567689/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.31
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| 520 |
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|a Prior experience in using Enterprise cloud is beneficial
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| 520 |
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|a 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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| 520 |
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|a 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.
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| 520 |
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|a 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 DescriptionEvery 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.
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