With countless options to design neural networks, an effective architecture search algorithm would be game-changing. Here we look at the state of the art.
This tutorial works through a step-by-step example of how the Levenshtein distance is calculated using dynamic programming.
In this article we explain what GauGANs are, and how their architecture and objective functions work. This is part of a series on Nvidia GauGANs.
An in-depth explanation of Gradient Descent, and how to avoid the problems of local minima and saddle points.
