Book Image

Hyperparameter Tuning with Python

By : Louis Owen
Book Image

Hyperparameter Tuning with Python

By: Louis Owen

Overview of this book

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.
Table of Contents (19 chapters)
Section 1:The Methods
Section 2:The Implementation
Section 3:Putting Things into Practice

Exploring Microsoft NNI

Neural Network Intelligence (NNI) is a package that is developed by Microsoft and can be utilized not only for hyperparameter tuning tasks but also for neural architecture search, model compression, and feature engineering. We discussed how to utilize NNI to conduct hyperparameter tuning experiments in Chapter 10, Advanced Hyperparameter Tuning with DEAP and Microsoft NNI.

In this section, we will discuss how to utilize this package to track those experiments. All of the experiment tracking modules provided by NNI are located in the web portal. You learned about the web portal in Chapter 10, Advanced Hyperparameter Tuning with DEAP and Microsoft NNI. However, we haven’t discussed it in depth and there are many useful features you should know about.

The web portal can be utilized to visualize all of the hyperparameter tuning experiment’s metadata, including (but not limited to) tuning and training progress, evaluation metrics, and error...