Version 2.0

Redesigned for more powerful networks (now supports autoencoders as well). Very easy to write small models (inspired by keras).

Features:

• Layers: Dense, Dropout, Flatten
• Activators: Sigmoid, Tanh, Relu
• Loss Functions: Quadratic, Hillinger, Cross-entropy
• Variable Initializers: Zeros, Normal, Xavier
• Optimizers: SGD, Momentum, RMSProp, AdaGrad, Adam

Version 1.0

First stable version. Tested on Windows, Linux and MacOS.