Автор: Ahmed Mr Masood Название: Ancient Astronomy-Allah`s Narrative ISBN: 1983512532 ISBN-13(EAN): 9781983512537 Издательство: Неизвестно Цена: 2340.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Tackling science narrators of our age such as Lawrence M. Krauss, Neil deGrasse Tyson, Sean M. Carroll and Richard Dawkins, this book appeals commonly to all students of science & religion, Atheists or otherwise. Clearly, it is a book tailor-made to serve every Christian, Jewish and Muslim house. A lucid illustration uncovering the mystery surrounding the face of God and faith. This book answers to the trending assertive of Naturalists (Atheists) claims of nothing from God has thus far reached us. The book takes the reader through origin sequences of the very beginnings of earth, its skies and the glitz in the dominant sky. A revelation of Abraham as a peer Astronomer-who earned a covenant of long poured out blessings on mankind and to whom Astronomers today owe dues. What this covenant means to human societies today and what is the argument of God. Scanning the promises of God to nations and His bestowal of hegemonies and legitimacy to rule. How are we performing amidst influential alien jinns and gatekeepers of heaven- the Angels? A mind-blowing telecast is drawn of jinns and the house of Angels in the backdrop of Cosmos we live surrounded in. The purposes for the grand scale of the universe as wide and as incomprehensible as it is. The discussion that, why it is at this Cosmic scale is herein thoroughly derived from the Quran. Grappling science mysteries of time travel and fathoming our next home in the celestial patch, it dispels great misconceptions associated with doomsday that it is not the collapse of the universe as wrongly thought, but it is a collapse of sun's neighbourhood and its neighbouring stars. So, what does this mean to us? The answer to this and many questions on life, liberty, and events post doomday are elucidated in this book.
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.
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