Isbn: 9781803235875 - hyperparameter tuning with python: boost your machine learning model's performance via hyperparameter tuning (19 risultati)

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Paperback or Softback. Condizione: New. Hyperparameter Tuning with Python: Boost your machine learning model's performance via hyperparameter tuning. Book.

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Paperback. Condizione: New. This book curates numerous hyperparameter tuning methods for Python all in one place, providing a deep explanation of how each method works, and a decision map that can help you choose which hyperparameter tuning method is right for your specific problem and situation.

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Paperback. Condizione: New. Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest detailsKey FeaturesGain a deep understanding of how hyperparameter tuning worksExplore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methodsLearn which method should be used to solve a specific situation or problemBook DescriptionHyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements.You'll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter.By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.What you will learnDiscover hyperparameter space and types of hyperparameter distributionsExplore manual, grid, and random search, and the pros and cons of eachUnderstand powerful underdog methods along with best practicesExplore the hyperparameters of popular algorithmsDiscover how to tune hyperparameters in different frameworks and librariesDeep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAPGet to grips with best practices that you can apply to your machine learning models right awayWho this book is forThis book is for data scientists and ML engineers who are working with Python and want to further boost their ML model's performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required. …

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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest detailsKey Features:Gain a deep understanding of how hyperparameter tuning worksExplore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methodsLearn which method should be used to solve a specific situation or problemBook Description:Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements.You'll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter.By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.What You Will Learn:Discover hyperparameter space and types of hyperparameter distributionsExplore manual, grid, and random search, and the pros and cons of eachUnderstand powerful underdog methods along with best practicesExplore the hyperparameters of popular algorithmsDiscover how to tune hyperparameters in different frameworks and librariesDeep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAPGet to grips with best practices that you can apply to your machine learning models right awayWho this book is for:This book is for data scientists and ML engineers who are working with Python and want to further boost their ML model's performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorrnrnLouis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, O.…

Taschenbuch. Condizione: Neu. Hyperparameter Tuning with Python | Boost your machine learning model's performance via hyperparameter tuning | Louis Owen | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781803235875 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …