Neural networks are the new gold standard of models for machine learning. Thanks to this structure, impressive progress has been made, from image analysis to speech recognition, and computers are now able to perform increasingly complex tasks. One surprising application of neural networks is their ability to model complex objects, such as images, text, or audio records, with fewer dimensions, while still preserving some aspects of the original dataset (shapes in the image, frequencies in the audio, and so on). In this section, following a quick general review of neural networks, we will focus on one architecture called skip-gram, which was first used in the context of word embedding but can be extended to graphs as well.
Artificial neural networks in a nutshell
Artificial neural networks were inspired by the human brain, where millions of neurons are connected to each other through synapses. The human brain is clearly adept at learning...
