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Hyperparameter Optimization in Machine Learning

58,84 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 18fc7d5c4906cc4b09a25f4fcf610c62e5
Autor: Agrawal, Tanay
Themengebiete: Artificial Itelligence Bayesian optimization Hyper Parameter Optimization Hyperas Hyperopt Hyperparameter Tuning Machine Learning Python Sequence model based optimization
Veröffentlichungsdatum: 29.11.2020
EAN: 9781484265789
Sprache: Englisch
Seitenzahl: 166
Produktart: Kartoniert / Broschiert
Verlag: APRESS
Untertitel: Make Your Machine Learning and Deep Learning Models More Efficient
Produktinformationen "Hyperparameter Optimization in Machine Learning"
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 LearnDiscover how changes in hyperparameters affect the model’s performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For  Professionals and students working with machine learning.

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