Mitra, Niloy J.Ritschel, TobiasKokkinos, IasonasGuerrero, PaulKim, VladimirRematas, KonstantinosYumer, ErsinRitschel, Tobias and Telea, Alexandru2018-04-142018-04-1420181017-4656https://doi.org/10.2312/egt.20181029https://diglib.eg.org:443/handle/10.2312/egt20181029In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. More recently, other domains such as geometry processing, animation, video processing, and physical simulations have benefited from deep learning methods as well. The massive volume of research that has emerged in just a few years is often difficult to grasp for researchers new to this area. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.Computing methodologiesNeural networksComputer graphics10.2312/egt.2018102913-15