Loss Function
Find out the difference between loss functions for regression and classification and which loss function is best for the MNIST classifier.
We'll cover the following...
Mean squared error vs. binary cross-entropy loss
In the last section, we developed a neural network to classify images of hand-written digits. Even though we intentionally designed the network to be simple, it worked remarkably well, getting an accuracy score of about 87% with the MNIST test dataset.
Here we’ll explore some refinements which can help us improve a network’s performance. Some neural networks are designed to produce a continuous range ...