This is an implementation for a neural network training on an MNIST model using python lists. Although not practical at all for real life, it was a nice exercise in combining knowledge of data structures and algorithms and incorporating this knowledge into the field of Machine Learning. I had a blast :) . Weights and Hidden layers from a fully trained Tensorflow model and fed as either weights for the model or for testing purposes by comparing with the outputs of the hidden layers of my Forward Propagating Neural Network. At a future point, including back-propagation to incorporate a fully-trainable model are not out of the picture as it would only require backwards functionality for the 2 layer classes.
hidden_layers - hidden layers outputs of tensorflow model that was used for verification
weights - weights of tensorflow model to be used as OrtegaDenseNetwork weights
Ortega_Feed_Forward_Network.ipynb - Jupyter Notebook displaying end product.