finelybook
Automated Machine Learning: Hyperparameter optimization,neural architecture search,and algorithm selection with cloud platforms
by 作者: Adnan Masood and Ahmed Sherif
Publisher Finelybook 出版社: Packt Publishing (February 18,2021)
Language 语言: English
pages 页数: 312 pages
ISBN-10 书号: 1800567685
ISBN-13 书号: 9781800567689
Book Description
Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning
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 more. You’ll explore different ways of implementing these techniques in open-source tools. Next,you’ll focus on enterprise tools,learning different ways of implementing AutoML in three major cloud service providers,including Microsoft Azure,Amazon Web Services (AWS),and the Google Cloud Platform. As you progress,you’ll explore the features of cloud AutoML platforms by 作者: building machine learning models using AutoML. Later chapters will show you how to develop accurate models by 作者: automating time-consuming and repetitive tasks involved in the machine learning development lifecycle.
By the end of this book,you’ll be able to build and deploy automated machine learning models that are not only accurate,but also increase productivity,allow interoperability,and minimize featuring 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 and differentiate between cloud and OSS offerings
Implement AutoML in tools such as AWS,Azure,and GCP and while deploying 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
