
Automated Machine Learning in Action
Manning Publications (Publisher)
Published on 1. June 2022
Book
Paperback/Softback
300 pages
978-1-61729-805-9 (ISBN)
Description
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.
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.
Reviews / Votes
"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 Lopez
"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 Rajagopal
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 234 mm
Width: 188 mm
Thickness: 21 mm
Weight
516 gr
ISBN-13
978-1-61729-805-9 (9781617298059)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.