pyfit 0.1.1

A minimalist neural networks library written for educational purposes

pyfit

pyfit is a minimalist neural networks library written from scratch in Python for educational purposes.

Overview

This project aims to:

• help Machine Learning students and enthusiasts get a deeper understanding of neural networks ;
• demonstrate automatic differentiation, a core concept of modern Deep Learning frameworks like PyTorch and TensorFlow ;
• define a clean, pythonic API and follow good coding practices, including type annotations and unit tests.

This material is used in the Machine Learning course taught at ENSC. ENSEIRB-MATMECA and IOGS. See also Acknowledgments.

Demonstration

See the demo notebook.

Features

• Autograd [ source | tests ]
• Neural Networks API [ source | tests (soon!) ]
• Losses [ source | tests ]
• Optimizers [ source | tests (soon!) ]
• Data Utilities [ source | tests (soon!) ]
• Metrics (soon!)
• Training (soon!)

Development Notes

Checking the code

pyfit uses the following tools:

• black for code formatting.
• pylint and mypy for linting.
• pytest for testing.

Run the following commands in project root folder to check the codebase.

> python -m pylint ./pyfit # linting (including type checks)
> python -m mypy .         # type checks only
> python -m pytest         # test suite

Uploading the package to PyPI

> python setup.py sdist bdist_wheel
> python -m twine upload dist/* --skip-existing