Automated Machine Learning in Action
Author: Qingquan Song ,Haifeng Jin,Xia Hu (Author)
Publisher Finelybook 出版社：Manning (May 3, 2022)
pages 页数：336 pages
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 Author: 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 Author: 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.
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 Author: manual processing. Author: 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.
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.