Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟭𝟭 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗚𝗿𝗶𝗱𝘀, 𝗚𝗿𝗼𝘂𝗽𝘀, 𝗚𝗿𝗮𝗽𝗵𝘀, 𝗚𝗲𝗼𝗱𝗲𝘀𝗶𝗰𝘀, 𝗮𝗻𝗱 𝗚𝗮𝘂𝗴𝗲𝘀 𝗯𝘆 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗼𝗳 𝗟𝗼𝗻𝗱𝗼𝗻 Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸This paper is published arxiv2022. 👉 This is a Geometric Deep learning End to End Tutorial. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 ✔️ 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 -- such as 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 local gradient-descent type methods, 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 pre-defined regularities arising from the underlying low-dimensionality and structure of the physical world. This text is concerned with exposing these regularities through unified geometric principles that can be applied throughout a wide spectrum of applications. ✔️ Such a 'geometric unification' endeavour, in the spirit of Felix Klein's Erlangen Program, serves a dual purpose: on one hand, it provides a common mathematical framework to study the most successful neural network architectures, such as CNN's, RNNs, GNNs, and Transformers. On the other hand, it gives a constructive procedure to incorporate prior physical knowledge into neural architectures and provide a principled way to build future architectures yet to be invented. #computervision #artificialintelligence #data