Automated Machine Learning in Action

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. Its 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 youre new to ML, youll 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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, youll 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.

What\'s inside

    Automatically tune model hyperparameters
    Pick the optimal pipeline components
    Select appropriate models and features
    Learn different search algorithms and acceleration strategies

About the reader
For ML novices building their first pipelines and experienced ML engineers looking to automate tasks.

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

Table of Contents
PART 1 FUNDAMENTALS OF AUTOML
1 From machine learning to automated machine learning
2 The end-to-end pipeline of an ML project
3 Deep learning in a nutshell
PART 2 AUTOML IN PRACTICE
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
PART 3 ADVANCED TOPICS IN AUTOML
7 Customizing the search method of AutoML
8 Scaling up AutoML
9 Wrapping up

Download pdf

ISBN-10

1617298050

ISBN-13

978-1617298059

Publisher

Manning

Price

47.38

File Type

PDF

Page No.

336

Qingquan Song

Discover more of the author’s books, see similar authors, read author blogs and more ...

Review

"A brilliant job! If you want to master automated machine learning then this is the guide you need."

Stanley Anozie, Cloudyrion

"Concise, easy-to-digest...holds the reader's hand from the very beginning and walks them through the concepts of AutoML. Highly recommended!"

Dimitris Polychronopoulos, AstraZeneca

"Finally, the definitive reference for AutoML with both real-world applications and theoretical bases."

Marco Carnini, Features Analytics

"Full of insights on a cutting-edge topic. Machine learning professionals will bring their skills to the next level."

Viton Vitanis, Viseca Payment Services

About the Author

Drs. Qingquan Song, Haifeng Jin, and Xia "Ben" Hu are the creators of the AutoKeras automated deep learning library. Dr. Song is currently a machine learning and relevance engineer in the AI Foundation team at LinkedIn. Dr. Jin is a software engineer on the Keras team at Google. They have both published papers at major data mining and machine learning conferences and journals. Dr. Hu is an associate professor at Rice University in the Department of Computer Science, whose work has been utilized by TensorFlow, Apple, and Bing.

Automated Machine Learning in Action

Machine Learning

Data Mining

Qingquan Song

Manning

Qingquan Song

Publishers opinion

Publishers opinion

Review

About the Author

Similar Books

Other Authors' Books

Other Publishing Books

User Reviews

Rating