Identify why deep learning is currently popular

Optimize and evaluate models using loss functions and performance metrics

Mitigate common problems that arise in machine learning

Create repeatable training, evaluation, and test datasets

Measure model performance using loss functions

Use loss functions as the basis for gradient descent

Optimize gradient descent to be as efficient as possible

Use performance metrics to make business decisions