Which loss function should you use?

Your team needs to build a model that predicts whether images contain a driver’s license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver’s licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: [‘driversjicense’, ‘passport’, ‘credit_card’].

Which loss function should you use?
A . Categorical hinge
B . Binary cross-entropy
C . Categorical cross-entropy
D . Sparse categorical cross-entropy

Answer: C

Explanation:

– **Categorical entropy** is better to use when you want to **prevent the model from giving more importance to a certain class**. Or if the **classes are very unbalanced** you will get a better result by using Categorical entropy.

– But **Sparse Categorical Entropy** is a more optimal coice if you have a huge amount of classes, enough to make a lot of memory usage, so since sparse categorical entropy uses less columns it **uses less memory**. https://stats.stackexchange.com/questions/326065/cross-entropy-vs-sparse-cross-entropy-when-to-use-one-over-the-other