60 Geometric Deep Learning Blueprint ( Special Edition)

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many highdimensional learning tasks previously thought to be beyond reach such as computer vision, playing Go, or protein folding are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradientdescent type methods, typically implemented as backpropagation. While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not uniform and have strong repeating patterns as a result of the lowdimensionality and structure of the physical world. Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks