In this tutorial, we will explore groundbreaking strategies, taking into account state-of-the-art shape representations using Neural Networks (NFs). We will present practical approaches to model and train NFs to approximate Signed Distance Functions (SDFs). We will also introduce methods for improving the training performance of NFs by implementing a strategy to dynamically sample data points during training. Finally, we will leverage the fact that an NF is a smooth function to analytically estimate the curvature measures of its level sets (no discretization is required).