Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient - Brossura

Agrawal, Tanay

Brossura

ISBN 10: 1484265785 ISBN 13: 9781484265789

Casa editrice: Apress, 2020

Riassunto

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.

Su questo libro

Informazioni sull?autore

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. 

Dalla quarta di copertina

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. 

You will:

• 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

Dettagli dell?edizione

• Editore: Apress
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1484265785
• ISBN 13: 9781484265789
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 188
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com