Today's project was to build a classifier to solve the problem of identifying specific handwriting samples from a dataset of images. Our hypothetical use case was to apply deep learning to enable customers of a restaurant chain to write their phone numbers in a simple iPad application, so that they could get a text notification that their party was ready to be seated. Our specific task was to build the intelligence that would drive this application.
Revisit our success criteria: How did we do? Did we succeed? What was the impact of our success? Just as we defined success at the beginning of the project, these are the key questions that we need to ask as deep learning data scientists, as we look to wrap up a project.
Our MLP model accuracy hit 87.42%! Not bad, given the depth of the model and the hyperparameters that we chose at the beginning. See if...