Automated Machine Learning in Action

  • 5h 29m
  • Haifeng Jin, Qingquan Song, Xia Hu
  • Manning Publications
  • 2022

Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner.

In Automated Machine Learning in Action you will learn how to:

  • Improve a machine learning model by automatically tuning its hyperparameters
  • Pick the optimal components for creating and improving your pipelines
  • Use AutoML toolkits such as AutoKeras and KerasTuner
  • Design and implement search algorithms to find the best component for your ML task
  • Accelerate the AutoML process with data-parallel, model pretraining, and other techniques

Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It’s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you’re new to ML, you’ll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning.

About the Author

Drs. Qingquan Song, Haifeng Jin, and Xia “Ben” Hu are the creators of the AutoKeras automated deep learning library.

In this Book

  • From Machine Learning to Automated Machine Learning
  • The End-to-End Pipeline of an ML Project
  • Deep Learning in a Nutshell
  • Automated Generation of End-to-End ML Solutions
  • Customizing the Search Space by Creating AutoML Pipelines
  • AutoML with a Fully Customized Search Space
  • Customizing the Search Method of AutoML
  • Scaling up AutoML
  • Wrapping up