# Hyperopt Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/hyperopt
> Markdown URL: https://aitinkerers.org/technologies/hyperopt.md
> Technology record last updated: 2026-02-25T10:18:29Z
> Generated: 2026-08-26T07:22:42Z

Hyperopt automates hyperparameter tuning in Python using Tree-of-Parzen Estimators (TPE) to navigate complex, high-dimensional search spaces.

This library optimizes objective functions across awkward search spaces involving real-valued, discrete, and conditional dimensions. It leverages the TPE algorithm (and Adaptive TPE) to outperform standard grid or random search strategies. You can scale experiments across clusters using MongoDB or Spark: integration with Scikit-learn and XGBoost is standard. The engine handles the heavy lifting of model selection by intelligently sampling the most promising parameter sets to minimize loss.

- Official technology site: https://github.com/hyperopt/hyperopt
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AutoRAG](https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s)

Automatic hyperparameter optimization of your retrieval augmented generation pipeline. Stop fiddling around manually with chunk size, top k and so on. Let the machine do the tuning of your retrieval settings. Inspired by hyperparameter optimization in conventional ML.

- Event context: AI Tinkerers Berlin - March 21 — 2024-03-21 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s

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