Theory
A Review of Popular Deep Learning Architectures: AlexNet, VGG16, and GoogleNet
Dive into a review of three foundational deep-learning architectures. Discover their key contributions, strengths, and how they paved the way for modern AI advancements.
By
Vihar Kurama
Geometric Deep Learning Library Comparison
This article covers an in-depth comparison of different geometric deep learning libraries, including PyTorch Geometric, Deep Graph Library, and Graph Nets.
By
David Dodel
By
David Dodel
Neural Architecture Search Part 1: An Overview
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.
By
Anuj Sable
Measuring Text Similarity Using the Levenshtein Distance
This tutorial works through a step-by-step example of how the Levenshtein distance is calculated using dynamic programming.
Understanding GauGAN Part 1: Unraveling Nvidia's Landscape Painting GANs
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.
Intro to optimization in deep learning: Gradient Descent
An in-depth explanation of Gradient Descent, and how to avoid the problems of local minima and saddle points.
