pylearn-ml 1.2.0

About The Project

PyLearn implements machine learning features from scratch. It supports basic features of supervised and unsupervised learning.

You can

• create neural networks with dense layers, different activation functions and loss functions,
• cluster your data without previously known classes,
• classify data
• evaluate models
• and more

Read the Documentation for more information.

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Installation

Install PyLearn using pip:

pip install pylearn-ml

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Built With

The source code was built with Python, mainly using NumPy and Pandas.

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Requirements

Requirements can be found under docs/requirements.txt.

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Usage

Import the library:

import pylearn as pl

Most models have a fit and a predict function.

Just create a model, train it and use it for predictions.

model = pl.Model()

model.fit(x_train, y_train)
...
model.predict(y_test)

For details of usage, have a look at the examples folder.
Or read the Documentation

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Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please follow the Contributing guidelines.

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License

Distributed under the MIT License. See LICENSE for more information.

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