v_0.1.7

FInalized the example for MNIST and MNIST_Plus.

Added the ability to calculate accuracy during training as well as during prediction.

Added confusion matrix calculation and visualization functions in utils.py.

Lots of new activation and loss functions and speed improvements

1. Both forward feed and back propagation are now significantly faster, for both NumPy and Cupy versions.
2. Furthermore, several more activation and loss functions are also available now.

v_0.1.5

Support for CUDA is here via Cupy.

Slower than CPU for smaller networks but the benefits are very evident for larger networks with more than 1.5 Million parameters.

Tested on

• XPS i7 11800H + 3050 Ti,
• Google Colab K80
• Kaggle

v_0.1.2

minor change in readme again lol

Cosmetic changes to PyPi readme

The logo was not displaying on PyPi, which is fixed now.

Pilot release v_0.0.1

This is the first release of CrysX-NN (crysx_nn)