Hyperparameter optimization in machine learning : make your machine learning and deep learning models more efficient / Tanay Agrawal.
2021
Q325.5
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Title
Hyperparameter optimization in machine learning : make your machine learning and deep learning models more efficient / Tanay Agrawal.
Author
Agrawal, Tanay, author.
ISBN
9781484265796 (electronic bk.)
1484265793 (electronic bk.)
1484265785
9781484265789
1484265793 (electronic bk.)
1484265785
9781484265789
Published
[Berkeley] : Apress, [2021]
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-6579-6 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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 youll 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, youll 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 models 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.
Note
Includes index.
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text file
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Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 10, 2021).
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Hyperparameter optimization in machine learning.
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Table of Contents
Chapter 1: Hyperparameters
Chapter 2: Brute Force Hyperparameter Tuning
Chapter 3: Distributed Hyperparameter Optimization
Chapter 4: Sequential Model-Based Global Optimization and Its Hierarchical
Chapter 5: Using HyperOpt
Chapter 6: Hyperparameter Generating Condition Generative Adversarial Neural.
Chapter 2: Brute Force Hyperparameter Tuning
Chapter 3: Distributed Hyperparameter Optimization
Chapter 4: Sequential Model-Based Global Optimization and Its Hierarchical
Chapter 5: Using HyperOpt
Chapter 6: Hyperparameter Generating Condition Generative Adversarial Neural.