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Key benefits

Gain a deep understanding of how hyperparameter tuning works

Explore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methods

Learn which method should be used to solve a specific situation or problem

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.

Who is this book 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.

What you will learn

Discover hyperparameter space and types of hyperparameter distributions

Explore manual, grid, and random search, and the pros and cons of each

Understand powerful underdog methods along with best practices

Explore the hyperparameters of popular algorithms

Discover how to tune hyperparameters in different frameworks and libraries

Deep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAP

Get to grips with best practices that you can apply to your machine learning models right away

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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View plans & pricing

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Amazon Customer Nov 27, 2022

This book is a good book for an aspiring data scientist who are familiar with machine learning techniques and have briefly introduced themselves to what hyper-parameter optimization is. It discusses in detail a variety of hyper-parameter optimization techniques and when and how to put them into practice.It is a great book for a new learner trying to improve skills in hyper-parameter optimization. 7 broadly categorized hyper-parameter optimization techniques are explained very well and gives you the opportunity to learn hyper-parameter optimization in one place -thereby expediting your learning.

Amazon Verified review

Toni P Jan 22, 2024

Good book if you need more views how to get the ML model to better shape. All the best to the future.

Caitlin Nov 30, 2022

I really enjoyed the format, writing and content of this book. The author does a nice job of giving the high level explanation and low-level coding examples for a broad variety of hyperparameter tuning approaches, methods and packages. You're left with the knowledge that you know when to use which option and, most importantly, why. This is a really solid read for the beginner-intermediate machine learning practitioner to develop their intuition and understanding around the subject, and more advanced practitioners could also use this book as a refresher or to extend their knowledge of new hyperparameter tuning packages.

Yiqiao Yin Sep 02, 2022

Dror Feb 26, 2023

Machine learning (ML) and artificial intelligence have taken the world by storm and revolutionized entire fields such as computer vision and natural language processing. Building effective ML models requires choosing first and foremost the right architecture, and an essential part of this process is choosing an optimal or near-optimal set of hyperparameters. Due to the somewhat mechanical nature of hyperparameter optimization, its importance is often underestimated by academics and practitioners alike.This unique book serves as a comprehensive guide to hyperparameter optimization. It begins with an introduction to hyperparameter tuning, and describes the main techniques involved: exhaustive search, heuristic search, Bayesian optimization and multi-fidelity optimization. The second part of the book provides a practical and helpful overview of the top relevant frameworks, such as scikit-learn, Hyperopt, Optuna, NNI and DEAP. The associated GitHub repository includes a useful collection of Colab Notebooks to demonstrate the implementation of the presented techniques.This practical book will benefit ML researchers, data scientists and software engineers who build and train ML models. It requires no more than a basic understanding of ML and some familiarity with the Python programming language. In return, the reader will gain a thorough understanding of one of the more important and underappreciated aspects of training ML models - hyperparameter tuning.Highly recommended!

Hyperparameter Tuning with Python: Boost your machine learning model's performance via hyperparameter tuning

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