Today’s reading list will be very short, I’m afraid, as I’m down in bed with Covid and have a horrible headache preventing me from reading anything.
Geometric foundations of Deep Learning: “Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance.” Can symmetry — a key concept in many scientific fields — help to bring an overarching concept and unifying concepts for the broad set of neural networks architecture? (
150+ Concepts Heard in Data Engineering: Data engineering is a fast-evolving field, full of many concepts and terms. There is parquet, yaml, clusters and nodes, hash functions, graph databases and time series databases. Easy to get lost. This article can quickly refresh your memory or even teach you new things. (
Data scientist | avid cyclist | amateur pianist (I'm sharing my personal opinion and experience, which should not to be considered professional advice)
