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Hyperparameter Optimization in Machine Learning

Make Your Machine Learning and Deep Learning Models More Efficient

Authors: Agrawal, Tanay

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  • Covers state-of-the-art techniques for hyperparameter tuning
  • Covers implementation of advanced Bayesian optimization techniques on machine learning algorithms to complex deep learning frameworks
  • Explains distributed optimization of hyperparameters, which increases the time efficiency of the model significantly 
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eBook 24,99 €
price for Spain (gross)
  • ISBN 978-1-4842-6579-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 31,19 €
price for Spain (gross)
  • ISBN 978-1-4842-6578-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.

This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you’ll discuss Bayesian optimization for hyperparameter search, which learns from its previous history.

The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you’ll focus on different aspects such as creation of search spaces and distributed optimization of these libraries.

Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.

Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. 

What You Will Learn

  • Discover how changes in hyperparameters affect the model’s performance.
  • Apply different hyperparameter tuning algorithms to data science problems
  • Work with Bayesian optimization methods to create efficient machine learning and deep learning models
  • Distribute hyperparameter optimization using a cluster of machines
  • Approach automated machine learning using hyperparameter optimization

Who This Book Is For 

Professionals and students working with machine learning.





About the authors

Tanay is a deep learning engineer and researcher, who graduated in 2019 in Bachelor of Technology from SMVDU, J&K. He is currently working at Curl Hg on SARA, an OCR platform. He is also advisor to Witooth Dental Services and Technologies. He started his career at MateLabs working on an AutoML Platform, Mateverse. He has worked extensively on hyperparameter optimization. He has also delivered talks on hyperparameter optimization at conferences including PyData, Delhi and PyCon, India. 

Table of contents (5 chapters)

Table of contents (5 chapters)
  • Introduction to Hyperparameters

    Pages 1-30

    Agrawal, Tanay

  • Hyperparameter Optimization Using Scikit-Learn

    Pages 31-51

    Agrawal, Tanay

  • Solving Time and Memory Constraints

    Pages 53-80

    Agrawal, Tanay

  • Bayesian Optimization

    Pages 81-108

    Agrawal, Tanay

  • Optuna and AutoML

    Pages 109-129

    Agrawal, Tanay

Buy this book

eBook 24,99 €
price for Spain (gross)
  • ISBN 978-1-4842-6579-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 31,19 €
price for Spain (gross)
  • ISBN 978-1-4842-6578-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
  • The final prices may differ from the prices shown due to specifics of VAT rules

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Bibliographic Information

Bibliographic Information
Book Title
Hyperparameter Optimization in Machine Learning
Book Subtitle
Make Your Machine Learning and Deep Learning Models More Efficient
Authors
Copyright
2021
Publisher
Apress
Copyright Holder
Tanay Agrawal
eBook ISBN
978-1-4842-6579-6
DOI
10.1007/978-1-4842-6579-6
Softcover ISBN
978-1-4842-6578-9
Edition Number
1
Number of Pages
XIX, 166
Number of Illustrations
49 b/w illustrations, 4 illustrations in colour
Topics