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

Understanding Population-Based Training

PBT is a population-based heuristic search method, just like the GA method and PSO. However, PBT is not a nature-inspired algorithm like GA or PSO. Instead, inspired by the GA method itself. PBT is suggested for when you are working with a neural-network-based type of model and just need the final trained model without knowing the specifically chosen hyperparameter configurations.

PBT is specifically designed to work only with a neural network-based type of models, such as a multilayer perceptron, deep reinforcement learning, transformers, GAN, and any other neural network-based models. It can be said that PBT does both hyperparameter tuning and model training since the weights of the neural network model are inherited during the process. So, PBT is not only for choosing the most optimal hyperparameter configurations but also for transferring the weights or parameters of the model to other individuals within the population. That’s...