Qingquan Song

Dr. Qingquan Song is a machine learning and relevance engineer in the AI Foundation team at LinkedIn. He received his PhD in computer science from Texas A&M University. His research interests are automated machine learning, dynamic data analysis, tensor decomposition, and their applications in recommender systems and social networks. He is one of the authors of AutoKeras. His papers have been published at major data mining and machine learning venues, including KDD, NeurIPS, Transactions on Knowledge Discovery from Data (TKDD), and others.

books by Qingquan Song

Automated Machine Learning in Action
  • March 2022
  • ISBN 9781617298059
  • 336 pages
Included with a Manning Online subscription
  • printed in black & white
  • Available translations: Korean, Simplified Chinese
catalog / Data Science / Machine Learning / Automated Machine Learning

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.