Hyperparameter Optimization - This Tutorial Is All You Need
Offered By: Abhishek Thakur via YouTube
Course Description
Overview
Explore various hyperparameter optimization techniques and libraries for tuning model parameters or optimizing any function in this comprehensive tutorial video. Learn about Grid Search, Random Search, Grid/Random Search with Pipelines, Bayesian Optimization with Gaussian Process, Hyperopt, and Optuna. Gain practical insights into implementing these methods to enhance model performance and efficiency. Follow along with detailed explanations and demonstrations of each technique, providing a solid foundation for applying hyperparameter optimization in machine learning projects.
Syllabus
Introduction
Grid Search
Random Search
Grid/Random Search with Pipelines
Bayesian Optimization with Gaussian Process
Hyperopt
Optuna
Taught by
Abhishek Thakur
Related Courses
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and OptimizationDeepLearning.AI via Coursera How to Win a Data Science Competition: Learn from Top Kagglers
Higher School of Economics via Coursera Predictive Modeling and Machine Learning with MATLAB
MathWorks via Coursera Machine Learning Rapid Prototyping with IBM Watson Studio
IBM via Coursera Hyperparameter Tuning with Neural Network Intelligence
Coursera Project Network via Coursera