Towards Geometric Deep Learning I: On the Shoulders of Giants

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach — computer vision, playing Go, or protein folding — are in fact feasible with appropriate computational scale. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning, whereby adapted, often hierarchical, features capture the appropriate notion of regularity for each task, and second, learning by gradient descent-type optimisation, typically implemented as backpropagation. While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not generic and come with essential predefined regularities… <a href="https://towardsdatascience.com/towards-geometric-deep-learning-i-on-the-shoulders-of-giants-726c205860f5">Click Here</a>