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 artificial neural network hyperparameters

An artificial neural network, also known as deep learning, is a kind of ML algorithm that mimics how human brains work. Deep learning can be utilized for both regression and classification tasks. One of the main selling points of this model is its ability to perform feature engineering and selection automatically from the raw data. In general, to ensure this algorithm works decently, we need a large amount of training data to be fed to the model. The simplest form of a neural network is called a perceptron (see Figure 11.4). A perceptron is just a linear combination that is applied on top of all of the features, with bias added at the end of the calculation:

Figure 11.4 – Perceptron

If the output from the perceptron is passed to a non-linear function, which is usually called an activation function, and then passed to another perceptron, then we can call this a multi-layer perceptron (MLP) with one layer...