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.
Автор: Michael R.W. Dawson Название: Mind, Body, World: Foundations of Cognitive Science ISBN: 1927356172 ISBN-13(EAN): 9781927356173 Издательство: Wiley EDC Рейтинг: Цена: 38890.00 T Наличие на складе: Невозможна поставка. Описание:
Cognitive science arose in the 1950s when it became apparent that anumber of disciplines, including psychology, computer science,linguistics, and philosophy, were fragmenting. Perhaps owing to thefield’s immediate origins in cybernetics, as well as to thefoundational assumption that cognition is information processing,cognitive science initially seemed more unified than psychology.However, as a result of differing interpretations of the foundationalassumption and dramatically divergent views of the meaning of the terminformation processing, three separate schools emerged:classical cognitive science, connectionist cognitive science, andembodied cognitive science.
Examples, cases, and research findings taken from the wide range ofphenomena studied by cognitive scientists effectively explain andexplore the relationship among the three perspectives. Intended tointroduce both graduate and senior undergraduate students to thefoundations of cognitive science, Mind, Body, World addressesa number of questions currently being asked by those practicing in thefield: What are the core assumptions of the three different schools?What are the relationships between these different sets of coreassumptions? Is there only one cognitive science, or are there manydifferent cognitive sciences? Giving the schools equal treatment anddisplaying a broad and deep understanding of the field, Dawsonhighlights the fundamental tensions and lines of fragmentation thatexist among the schools and provides a refreshing and unifyingframework for students of cognitive science.
Автор: Lakshmanan Valliappa, Robinson Sara, Munn Michael Название: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and Mlops ISBN: 1098115783 ISBN-13(EAN): 9781098115784 Издательство: Wiley Рейтинг: Цена: 55960.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: 60550.00 T 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.
