{"product_id":"automated-machine-learning-hyperparameter-optimization-neural-architecture-search-and-algorithm-selection-with-cloud-platforms-paperback","title":"Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eAdnan Masood\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eGet to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice\u003c\/li\u003e\n\u003cli\u003eEliminate mundane tasks in data engineering and reduce human errors in machine learning models\u003c\/li\u003e\n\u003cli\u003eFind out how you can make machine learning accessible for all users to promote decentralized processes\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eEvery 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore AutoML fundamentals, underlying methods, and techniques\u003c\/li\u003e\n\u003cli\u003eAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario\u003c\/li\u003e\n\u003cli\u003eFind out the difference between cloud and operations support systems (OSS)\u003c\/li\u003e\n\u003cli\u003eImplement AutoML in enterprise cloud to deploy ML models and pipelines\u003c\/li\u003e\n\u003cli\u003eBuild explainable AutoML pipelines with transparency\u003c\/li\u003e\n\u003cli\u003eUnderstand automated feature engineering and time series forecasting\u003c\/li\u003e\n\u003cli\u003eAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problems\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eCitizen 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 312\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.65 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 18, 2021\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43155075661887,"sku":"9781800567689","price":84.65,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/aogv08GkPp9781800567689.webp?v=1776961996","url":"https:\/\/dhlswag.com\/products\/automated-machine-learning-hyperparameter-optimization-neural-architecture-search-and-algorithm-selection-with-cloud-platforms-paperback","provider":"BBB","version":"1.0","type":"link"}