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Automated Machine Learning

You're reading from  Automated Machine Learning

Product type Book
Published in Feb 2021
Publisher Packt
ISBN-13 9781800567689
Pages 312 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Adnan Masood Adnan Masood
Profile icon Adnan Masood
LinkedIn
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.
See other products by Adnan Masood

Table of Contents (15) Chapters

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

AutoKeras

Keras is one of the most widely used deep learning frameworks and is an integral part of the TensorFlow 2.0 ecosystem. Auto-Keras is based on the paper by Jin et al., (https://arxiv.org/abs/1806.10282) which proposed "a novel method for efficient neural architecture search with network morphism, enabling Bayesian optimization". AutoKeras is built on the concept that since existing neural architecture search algorithms such as NASNet and PNAS are computationally quite expensive, using Bayesian optimization to guide the network's morphism is an efficient approach to explore the search space.

The toolkit is available on GitHub to be downloaded: github.com/jhfjhfj1/autokeras.

The following steps will guide you through how to install AutoKeras and how to run an automated ML experiment using the library. Let's get started:

  1. To get started with Auto-Keras, run the following install commands in Colab or in a Jupyter Notebook. Doing this will install...
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Authors (1)

Profile icon Adnan Masood
LinkedIn
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.
See other products by Adnan Masood

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