Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, C++, .NET, Ada, Python, Delphi, Octave, Ruby, Prolog Pure Data and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library.
J. Tritscher, D. Schlör, F. Gwinner, A. Krause, and A. Hotho. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Communications in Computer and Information Science (1753):
79-96(2022)
J. Tritscher, D. Schlör, F. Gwinner, A. Krause, and A. Hotho. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Communications in Computer and Information Science (1753):
79-96(2022)
S. Merity. (2019)cite arxiv:1911.11423Comment: Addition of citations and contextual results (no attention head, single attention head, attention per layer), removal of wordpiece WikiText-103 numbers due to normalization issues, fix of SHA attention figure Q arrow, other minor fixes.