Overview:

HyperLib is a library that implements common Neural Network components in the hyperbolic space using the Poincare model. It is designed to be used with Tensorflow as a backend and can easily be integrated with Keras. The purpose of this library is to assist Data Scientists, Machine Learning Engineers, Researchers, and others in implementing hyperbolic neural networks. Additionally, the library provides mathematical functions for utilizing hyperbolic space for purposes other than neural networks.

Features:

Hyperbolic Neural Networks: HyperLib allows users to create and train hyperbolic neural networks using Keras.
Mathematical Functions: The library provides mathematical functions based on the Poincare model, enabling users to work with hyperbolic space for various purposes.
Hierarchical Representation: Hyperbolic space is advantageous for representing hierarchical data, and HyperLib supports techniques for embedding data in hyperbolic space.
Compatibility: HyperLib is compatible with python>=3.8 and tensorflow>=2.0.

Summary:

HyperLib is a powerful library for implementing neural networks in the hyperbolic space. With its compatibility with Tensorflow and easy integration with Keras, it provides a convenient solution for creating hyperbolic neural networks. Moreover, the library offers mathematical functions and techniques for embedding hierarchical data in hyperbolic space. By leveraging the advantages of hyperbolic space, HyperLib can be a valuable tool for Data Scientists, Machine Learning Engineers, Researchers, and others in the field.

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Hyperlib