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Automated Machine Learning

You're reading from   Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800567689
Length 312 pages
Edition 1st Edition
Languages
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Author (1):
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Adnan Masood Adnan Masood
Author Profile Icon Adnan Masood
Adnan Masood
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Automated Machine Learning
2. Chapter 1: A Lap around Automated Machine Learning FREE CHAPTER 3. Chapter 2: Automated Machine Learning, Algorithms, and Techniques 4. Chapter 3: Automated Machine Learning with Open Source Tools and Libraries 5. Section 2: AutoML with Cloud Platforms
6. Chapter 4: Getting Started with Azure Machine Learning 7. Chapter 5: Automated Machine Learning with Microsoft Azure 8. Chapter 6: Machine Learning with AWS 9. Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot 10. Chapter 8: Machine Learning with Google Cloud Platform 11. Chapter 9: Automated Machine Learning with GCP 12. Section 3: Applied Automated Machine Learning
13. Chapter 10: AutoML in the Enterprise 14. Other Books You May Enjoy

AutoGluon – the AutoML toolkit for deep learning

From AWS Labs, with the goal of democratization of ML in mind, AutoGluon is described as being developed to enable "easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications spanning image, text, or tabular data". AutoGluon, an integral part of AWS's automated ML strategy, enables both junior and seasoned data scientists to build deep learning models and end-to-end solutions with ease. Like other automated ML toolkits, AutoGluon offers network architecture search, model selection, and the ability for you to improve custom models.

The toolkit is available on GitHub to be downloaded: https://github.com/awslabs/autogluon.

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