Dynamic neural network library
DyNet (formerly known as cnn) is a neural network library developed by Carnegie Mellon University and many others. It is written in C++ (with bindings in Python) and is designed to be efficient when run on either CPU or GPU, and to work well with networks that have dynamic structures that change for every training instance. For example, these kinds of networks are particularly important in natural language processing tasks, and DyNet has been used to build state-of-the-art systems for syntactic parsing, machine translation, morphological inflection, and many other application areas.
Read the documentation to get started, and feel free to contact the dynet-users group group or github page with any questions, issues, or contributions.
- Latest Code: Can be found on the github page master branch.
- Latest Release: v1.0-rc1