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.

潜心研究机器学习模型的超参数调优，关注什么是超参数以及它们如何工作。本书讨论了超参数调整的不同技术，从基础知识到高级方法。

这是一本循序渐进的超参数优化指南，首先介绍了什么是超参数以及它们如何影响机器学习模型的不同方面。然后，它介绍了超参数优化的一些基本（蛮力）算法。此外，作者使用分布式优化方法，解决了时间和内存限制的问题。接下来你会讨论超参数搜索的贝叶斯优化，它可以从以前的历史中学习。

本书讨论了不同的框架，如Hyperopt和Optuna，它实现了基于模型的顺序全局优化（SMBO）算法。在这些讨论中，你将专注于不同的方面，如搜索空间的创建和这些库的分布式优化。

机器学习中的超参数优化》让你了解这些算法是如何工作的，以及你如何在现实生活中的数据科学问题中使用它们。最后一章总结了超参数优化在自动化机器学习中的作用，并以创建自己的AutoML脚本的教程结束。

超参数优化是一项繁琐的工作，所以请坐下来，让这些算法为你工作。

你将学到什么

发现超参数的变化如何影响模型的性能。

在数据科学问题上应用不同的超参数调整算法

使用贝叶斯优化方法来创建高效的机器学习和深度学习模型

使用机器集群分布式超参数优化

使用超参数优化接近自动化机器学习

本书适用对象

从事机器学习的专业人士和学生