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mikkokotila committed Apr 22, 2024
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<h3 align="center">Hyperparameter Optimization for TensorFlow and Keras</h3>

<p align="center">

<a href="https://travis-ci.org/autonomio/talos">
<img src="https://img.shields.io/travis/autonomio/talos/master.svg?style=for-the-badge&logo=appveyor" alt="Talos Travis">
</a>

<a href="https://coveralls.io/github/autonomio/talos">
<img src="https://img.shields.io/coveralls/github/autonomio/talos.svg?style=for-the-badge&logo=appveyor" alt="Talos Coveralls">
</a>

</p>
<h3 align="center">Bullet-Proof Hyperparameter Experiments for TensorFlow and Keras</h3>

<p align="center">
<a href="#talos">Talos</a> •
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Talos is made for researchers, data scientists, and data engineers that want to remain in **complete control of their TensorFlow (tf.keras) and Keras models**, but are tired of mindless parameter hopping and confusing optimization solutions that add complexity instead of reducing it.

**Within minutes, without learning any new syntax,** Talos allows you to configure, perform, and evaluate hyperparameter experiments that yield state-of-the-art results across a wide range of prediction tasks. Talos provides the **simplest and yet most powerful** available method for hyperparameter optimization with TensorFlow (tf.keras) and Keras.

<hr>

### :wrench: Key Features

Based on what no doubt constitutes a "biased" review (being our own) of more than ~30 hyperparameter tuning and optimization solutions, Talos comes on top in terms of intuitive, easy-to-learn, highly permissive access to critical hyperparameter experimentation capabilities. Key features include:
**Within minutes, without learning any new syntax,** Talos allows you to configure, perform, and evaluate hyperparameter experiments that yield state-of-the-art results across a wide range of prediction tasks. Talos provides the **simplest and yet most powerful** available method for hyperparameter optimization with TensorFlow (tf.keras) and Keras. Key features include:

- Single-line optimize-to-predict pipeline `talos.Scan(x, y, model, params).predict(x_test, y_test)`
- Automated hyperparameter optimization
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