Learning-Deep-Learning

Why we included this project

Most paper lists hand you links and call it done. This one is a working engineer's reading notes, with the reasoning left in. The author leads ML modeling for an end-to-end autonomous driving effort at Nvidia, and his notes on perception topics like BEV, 3D object detection, semantic occupancy prediction, and drivable space carry weight from production work, not just theory. Newcomers get a clear place to start in the suggested first-CNN-papers list, while more experienced readers can work through the topic-organized notes that connect foundational ideas to the problems self-driving systems deal with in practice. It is a study companion rather than software to run, and for anyone learning CV or ML it is worth a close read.