Automated Machine Learning in Action - Softcover

Song, Qingquan; Jin, Haifeng; Hu, Xia

Softcover

ISBN 10: 1617298050 ISBN 13: 9781617298059

Publisher: Manning Publications, 2022

Synopsis

Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner. Automated Machine Learning in Action, filled with hands-onexamples and written in an accessible style, reveals how premade machine learning components can automate time-consuming ML tasks.

Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input. You'll quickly run through machine learning basics thatopen upon AutoML to non-data scientists, before putting AutoML into practicefor image classification, supervised learning, and more.

Automated machine learning (AutoML) automates complex andtime-consuming stages in a machine learning pipeline with pre packaged optimal solutions. This frees up data scientists from data processing and manualtuning, and lets domain experts easily apply machine learning models to their projects.

About this title

About the Author

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.

From the Back Cover

Automated Machine Learning in Action teaches you to automate your machine learning pipelines with AutoKeras and Keras Tuner.Written by the creators of the AutoKeras system, it's full of AutoML techniques and advanced toolkits for optimizing how your machine learning models function.

AutoML concepts and techniques are introduced through real-world examples and practical code snippets―no complex math or formulas. You'll quickly run through machine learning basics that open upon AutoML to non-data scientists, before putting AutoML into practice for image classification, supervised learning, and more. You'll learn to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input.

Edition details

• Publisher: Manning Publications
• Publication date: 2022
• Language: English
• ISBN 10: 1617298050
• ISBN 13: 9781617298059
• Binding: Paperback
• Edition number: 1
• Number of pages: 300