  Deep neural networks (DNN) are black box algorithms. They are trained using a
gradient descent back propagation technique which trains weights in each layer
for the sole goal of minimizing training error. Hence, the resulting weights
cannot be directly explained. Using Topological Data Analysis (TDA) we can get
an insight on how the neural network is thinking, specifically by analyzing the
activation values of validation images as they pass through each layer.
