Automated Machine Learning in Action

Song, Qingquan, Jin, Haifeng, Hu, Xia

  • 出版商: Manning
  • 出版日期: 2022-06-07
  • 售價: $2,060
  • 貴賓價: 9.5$1,957
  • 語言: 英文
  • 頁數: 336
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617298050
  • ISBN-13: 9781617298059
  • 相關分類: Machine Learning 機器學習
  • 下單後立即進貨 (約1週~2週)



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 technology

Machine learning tasks like data pre-processing, feature selection, and model optimization can be time-consuming and highly technical. Automated machine learning, or AutoML, applies pre-built solutions to these chores, eliminating errors caused by manual processing. By accelerating and standardizing work throughout the ML pipeline, AutoML frees up valuable data scientist time and enables less experienced users to apply machine learning effectively.

about the book

Automated Machine Learning in Action shows you how to save time and get better results using AutoML. As you go, you’ll learn how each component of an ML pipeline can be automated with AutoKeras and KerasTuner. The book is packed with techniques for automating classification, regression, data augmentation, and more. The payoff: Your ML systems will be able to tune themselves with little manual work.

Product description


“Automating automation itself is a new concept and this book does justice to it in terms of explaining the concepts, sharing real world advancements, use cases and research related to the topic. “ Satej KumarSahu

“A book with a lot of promise, covering a topic that's like to become hot in the next year or so. Read this now, and get ahead of the curve!” RichardVaughan

“A nice introduction to AutoML, its ambitions, and challenges bothin theory and in practice.” Alain Couniot

“Helps you to clearly understand the process of Machine Learning automation. The examples are clear, concise, and applicable to the real world.”Walter Alexander Mata López

“The author's friendly style makes novices feel ready to try outAutoML tools.” Gaurav Kumar Leekha

“A great book to take your machine learning skills to the next level.” Harsh Raval

“An impressive effort by the authors to break down a complex MLtopic into understandable chunks.” Venkatesh RajagopalTable of Contents


Qingquan Song, Haifeng Jin, and Dr. Xia "Ben" Hu are the creators of the AutoKeras automated deep learning library. Qingquan and Haifeng are PhD students at Texas A&M University, and have both published papers at major data mining conferences and journals. Dr. Hu is an associate professor at Texas A&M University in the Department of Computer Science and Engineering, whose work has been utilized by TensorFlow, Apple, and Bing.


Table of Contents
1 From machine learning to automated machine learning
2 The end-to-end pipeline of an ML project
3 Deep learning in a nutshell
4 Automated generation of end-to-end ML solutions
5 Customizing the search space by creating AutoML pipelines
6 AutoML with a fully customized search space
7 Customizing the search method of AutoML
8 Scaling up AutoML
9 Wrapping up