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Automated machine learning in action, qingquan song, haifeng jin, xia hu

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Optimize every stage of your machine learning pipelines with powerful
automation components and cutting-edge tools like AutoKeras and KerasTuner. \n
\nIn Automated Machine Learning in Action you will learn how to: \n \nImprove
a machine learning model by automatically tuning its hyperparameters \nPick
the optimal components for creating and improving your pipelines \nUse AutoML
toolkits such as AutoKeras and KerasTuner \nDesign and implement search
algorithms to find the best component for your ML task \nAccelerate the AutoML
process with data-parallel, model pretraining, and other techniques \n
\nAutomated 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. \n \nAbout
the technology \nMachine 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. \n \nAbout the book \nAutomated 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. \n \nWhat's inside \n
\nAutomatically tune model hyperparameters \nPick the optimal pipeline
components \nSelect appropriate models and features \nLearn different search
algorithms and acceleration strategies \n \nAbout the reader \nFor ML novices
building their first pipelines and experienced ML engineers looking to
automate tasks. \n \nAbout the author \nDrs. Qingquan Song, Haifeng Jin, and
Xia “Ben” Hu are the creators of the AutoKeras automated deep learning
library. \n \nTable of Contents \nPART 1 FUNDAMENTALS OF AUTOML \n1 From
machine learning to automated machine learning \n2 The end-to-end pipeline of
an ML project \n3 Deep learning in a nutshell \nPART 2 AUTOML IN PRACTICE \n4
Automated generation of end-to-end ML solutions \n5 Customizing the search
space by creating AutoML pipelines \n6 AutoML with a fully customized search
space \nPART 3 ADVANCED TOPICS IN AUTOML \n7 Customizing the search method of
AutoML \n8 Scaling up AutoML \n9 Wrapping up

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