This library is a collection of machine learning algorithms and neural network components implemented from scratch using only NumPy. It serves as an educational toolkit for constructing and experimenting with machine learning architectures, emphasizing a modular approach where algorithms are organized into self-contained, object-oriented classes.
The project distinguishes itself by relying exclusively on array-oriented programming to perform mathematical operations, ensuring that all computations are vectorized for performance. By utilizing a standardized interface for forward and backward passes, the library allows users to chain independent components to build custom models without the overhead of larger, more complex frameworks.
The software covers a broad range of data science capabilities, including linear and probabilistic modeling, matrix factorization, and tree-based learning. It also provides specialized tools for nonparametric estimation, reinforcement learning, and deep learning, alongside a suite of preprocessing utilities for signal and text data.
