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few small changes to README.md #600

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Apr 22, 2024
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few small changes to README.md #600

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baae368
few small changes to README.md
mikkokotila Apr 22, 2024
d346852
improved the tagline
mikkokotila Apr 22, 2024
d74eb16
replace quantum with uniform in tests
mikkokotila Apr 22, 2024
715d2b6
change the mentioned stats in readme
mikkokotila Apr 22, 2024
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20 changes: 3 additions & 17 deletions README.md
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Expand Up @@ -4,19 +4,7 @@
<br>
</h1>

<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 with TensorFlow and Keras</h3>

<p align="center">
<a href="#talos">Talos</a> •
Expand Down Expand Up @@ -51,13 +39,11 @@ TL;DR Thousands of researchers have found Talos to importantly improve ordinary

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
Expand All @@ -83,7 +69,7 @@ Get the below code [here](https://gist.github.com/mikkokotila/4c0d6298ff0a22dc56

<img src=https://i.ibb.co/VWd8Bhm/Screen-Shot-2019-01-06-at-11-26-32-PM.png>

The *Simple* example below is more than enough for starting to use Talos with any Keras model. *Field Report* has +2,600 claps on Medium because it's more entertaining.
The *Simple* example below is more than enough for starting to use Talos with any Keras model. *Field Report* has +4,400 claps on Medium because it's more entertaining.

[Simple](https://nbviewer.jupyter.org/github/autonomio/talos/blob/master/examples/A%20Very%20Short%20Introduction%20to%20Hyperparameter%20Optimization%20of%20Keras%20Models%20with%20Talos.ipynb) [1-2 mins]

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2 changes: 1 addition & 1 deletion tests/commands/test_templates.py
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Expand Up @@ -40,7 +40,7 @@ def test_templates():
x, y = talos.templates.datasets.cervical_cancer()
x, y = talos.templates.datasets.titanic()

talos.templates.pipelines.breast_cancer(random_method='quantum')
talos.templates.pipelines.breast_cancer(random_method='uniform_mersenne')
talos.templates.pipelines.cervical_cancer(random_method='sobol')
talos.templates.pipelines.iris(random_method='uniform_crypto')
talos.templates.pipelines.titanic(random_method='korobov_matrix')
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