Udemy - Hyperparameter Optimization for Machine Learning 2021

Table of Contents

Description

Hyperparameter Optimization for Machine Learning is a hyperparameter optimization training course for machine learning published by Udemy Academy. This course covers a number of topics, the most important of which are network search, random search, Bayesian optimization, multi-fidelity models, Optuna framework, Hyperopt library, and Scikit-Optimize. And…

pointed out. In this training course, you will be introduced to various techniques for selecting the best and most optimal meta-parameter, and you will be able to improve the performance of machine learning models as much as possible. There are several approaches to optimizing meta-parameters, and in this course, you will learn about the advantages and disadvantages, as well as the functional considerations of each.

What you will learn in the Hyperparameter Optimization for Machine Learning course:

Adjust and optimize the meta parameter and understand its importance and why
Cross-validation method
Adjustment and optimization of meta-parameters with the methods of network search method and the random and random search
Bayesian optimization
Tree-Structured Parzen Estimators Optimization Approach
Population-Based Training
Hyperopt, Optuna , Scikit-optimize, and Keras Turner libraries and frameworks