{ "item_title" : "Automated Machine Learning in Action", "item_author" : [" Qingquan Song", "Haifeng Jin", "Xia Hu "], "item_description" : "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 hyperparametersPick the optimal components for creating and improving your pipelinesUse AutoML toolkits such as AutoKeras and KerasTunerDesign and implement search algorithms to find the best component for your ML taskAccelerate 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 by 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technologyMachine 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 bookAutomated 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. What's inside Automatically tune model hyperparametersPick the optimal pipeline componentsSelect appropriate models and featuresLearn different search algorithms and acceleration strategies About the readerFor ML novices building their first pipelines and experienced ML engineers looking to automate tasks. About the authorDrs. Qingquan Song, Haifeng Jin, and Xia Ben Hu are the creators of the AutoKeras automated deep learning library. Table of ContentsPART 1 FUNDAMENTALS OF AUTOML1 From machine learning to automated machine learning2 The end-to-end pipeline of an ML project3 Deep learning in a nutshellPART 2 AUTOML IN PRACTICE4 Automated generation of end-to-end ML solutions5 Customizing the search space by creating AutoML pipelines6 AutoML with a fully customized search spacePART 3 ADVANCED TOPICS IN AUTOML7 Customizing the search method of AutoML8 Scaling up AutoML9 Wrapping up", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/61/729/805/1617298050_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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