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.
