SimpleNN is a Simple Neural Network implementation in the idea of computational graph using pure python3.
Similar to TensorFlow, the graph nodes represent mathematical operations, while the graph edges represent the data arrays (numpy) that flow between them. This repo is for academic usage only, no guarantee, although will try, for performance and accuracy.
This computation graph framework was designed by Philipp Meerkamp, Pierre Garapon, and David Rosenberg. This is a python3 implementation by Yi Zhou after taking David Rosenberg's DS-GA 1003 Machine Learning Course at NYU Data Science Center and for the sake of CS-GY 6643 Computer Vision Project.
Creative Commons Attribution 4.0 International Public License