Description:

Hype is a proof-of-concept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization.

This is enabled by nested automatic differentiation (AD) giving you access to the automatic exact derivative of any floating-point value in your code with respect to any other. Underlying computations are run by a BLAS/LAPACK backend (OpenBLAS by default).

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows

Data Formats: Agnostic

Tags: Deep Learning, Neural Networks, Optimization, Hyperparameter

Archive: download here

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