Interactive Tutorial#

For hands-on learning, we provide a comprehensive Jupyter notebook tutorial that covers all aspects of Hyperactive.

Tutorial Notebook#

This interactive notebook covers:

Basic optimization concepts — Understanding search spaces and objective functions
All optimizer categories — Hands-on examples with each algorithm type
Real-world ML examples — Practical hyperparameter optimization workflows
Best practices and tips — Common pitfalls and how to avoid them

Running the Tutorial#

You can run the tutorial locally:

# Clone the tutorial repository
git clone https://github.com/SimonBlanke/hyperactive-tutorial.git
cd hyperactive-tutorial

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook notebooks/hyperactive_tutorial.ipynb

Or view it directly on nbviewer without any installation.

Additional Resources#

Hyperactive GitHub Repository
Gradient-Free-Optimizers — The underlying optimization library
User Guide — Detailed documentation of all features