  When training or evaluating deep learning models, two essential parts are
picking the proper loss function and deciding on performance metrics. In this
paper, we provide a comprehensive overview of the most common loss functions
and metrics used across many different types of deep learning tasks, from
general tasks such as regression and classification to more specific tasks in
Computer Vision and Natural Language Processing. We introduce the formula for
each loss and metric, discuss their strengths and limitations, and describe how
these methods can be applied to various problems within deep learning. We hope
this work serves as a reference for researchers and practitioners in the field,
helping them make informed decisions when selecting the most appropriate loss
function and performance metrics for their deep learning projects.
