Overview
- 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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Table of contents (5 chapters)
Keywords
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.Reviews
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Hyperparameter Optimization in Machine Learning
Book Subtitle: Make Your Machine Learning and Deep Learning Models More Efficient
Authors: Tanay Agrawal
DOI: https://doi.org/10.1007/978-1-4842-6579-6
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Tanay Agrawal 2021
Softcover ISBN: 978-1-4842-6578-9Published: 29 November 2020
eBook ISBN: 978-1-4842-6579-6Published: 28 November 2020
Edition Number: 1
Number of Pages: XIX, 166
Number of Illustrations: 49 b/w illustrations, 4 illustrations in colour
Topics: Machine Learning, Python, Open Source