Quick-Step-by-Step-Tutorial-on-Bayesian-Optimisation-

This repository contains Jupyter notebooks demonstrating Bayesian Optimization (BO) for various applications. The notebooks cater to both researchers and beginners, offering a comprehensive guide and simplified examples.

Notebooks Included

1. BO_A_Comprehensive_Guide_for_Researchers.ipynb: An in-depth guide to BO for researchers, covering Gaussian Processes, acquisition functions, and advanced techniques.
2. BO_A_Simple_Guide_for_Everyone.ipynb: A beginner-friendly introduction to BO with step-by-step examples.
3. RF_BO_Tuning.ipynb: Demonstrates BO for tuning Random Forest hyperparameters.
4. ANN_BO_Tuning.ipynb: Focuses on tuning Artificial Neural Network hyperparameters using BO.

Getting Started

To run the notebooks, ensure you have Python 3.7+ and install the required libraries:

pip install numpy pandas scikit-learn GPyOpt matplotlib seaborn