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 provided for other languages, allowing Le to be used in C++, Rust, and Python programs.
Le is currently under active development. New features and updates are on the way, so 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 CUDA.
- Apple Metal.
Copyright © 2017 Kyrylo Polezhaiev. All rights reserved.
Le is released under the MIT License.