# Keras Tuner Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/keras-tuner
> Markdown URL: https://aitinkerers.org/technologies/keras-tuner.md
> Technology record last updated: 2026-02-25T10:19:24Z
> Generated: 2026-09-23T01:46:07Z

Keras Tuner is a scalable hyperparameter optimization framework that automates the search for ideal model architectures and training configurations.

This library eliminates manual trial and error by systematically exploring search spaces for TensorFlow and Keras models. Users implement the kt.HyperParameters class to define ranges for specific variables: layer depth, dropout rates, or learning schedules. The engine supports four primary algorithms (BayesianOptimization, Hyperband, RandomSearch, and a Sklearn wrapper) to identify peak performance. By managing execution logs and model checkpointing automatically, it isolates the exact configuration that maximizes validation accuracy while minimizing resource overhead.

- Official technology site: https://keras.io/keras_tuner/
- 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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