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le

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Low-level Machine Learning Library

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# Le - Machine Learning Library

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**Le** is a low-level machine learning library designed for readability and ease of use. Written in pure C, it, however, employs an object-oriented approach through GObject.

Bindings are available for other languages, allowing **Le** to be used in C++ and Python programs, with Rust support planned for the future.

I originally created **Le** as a learning project while studying Andrew Ng's Deep Learning course. At the time, I didn’t feel confident or fluent in Python, so I chose to implement everything in C to gain a deeper understanding and maintain control. Today, I use it to re-implement or experiment with models from research papers or open-source releases.

The name **Le** is short for "Learning". **Le** is currently under active development, with new features and updates on the way. Stay tuned!

At this moment following ML models are implemented:
* Polynomial Regression.
* Support Vector Machines (SVM).
* Sequential Feed-forward Neural Network (Multiple Layer Perceptron, MLP).
* k-Nearest Neighbors Algorithm (k-NN).

Optimization algorithms supported:
* Batch Gradient Descent (BGD).
* Stochastic Gradient Descent (SGD) with momentum.
* Sequential Minimal Optimization (SMO).

Supported backends:
* Nvidia's CUDA.
* Apple's Metal Performance Shaders (MPS).

## Installation

* [From Source](/docs/BUILD.md)
    
## Examples

* [C Examples](/examples/c)
* [C++ Examples](/examples/cpp)
* [Python Examples](/examples/python)

## Contribution

* [Code Style Guidelines](/docs/HACKING.md)

## Tools

* [Le Playground](/tools/playground);
* [Le MNIST Inspect](/tools/mnist-inspect).

## License

**Le** is released under the [MIT](LICENSE) License.

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