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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

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781800567689

Length 312 pages

Edition 1st Edition

Languages

Python

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Adnan Masood

View More author details

Table of Contents (15) Chapters

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

Further reading

For more information on the topics that were covered in this chapter, please refer to the following links and resources:

Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow, by Dr. Saket S.R. Mengle , Maximo Gurmendez, Packt Publishing: https://www.amazon.com/Mastering-Machine-Learning-AWS-TensorFlow/dp/1789349796
Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists, by Julien Simon and Francesco Pochetti, Packt Publishing: https://www.amazon.com/Learn-Amazon-SageMaker-developers-scientists/dp/180020891X

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Authors (1)

Adnan Masood

Adnan Masood

Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.

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