awesome-deeplearning-resources

Why we included this project

This is a practical reading list for anyone tracking deep learning and deep reinforcement learning research without wading through arXiv on their own. Papers are organized by year and by subfield, so you can go straight to computer vision, NLP, multi-modal, or reinforcement learning work from a particular period. The list also collects a model zoo with code links, pretrained models, courses, books, tutorials, and software, which helps when you need a reference implementation or a checkpoint to build on. The author marks the papers and software they consider more important or popular, a useful shortcut when you are new to an area. It is a curated index rather than a runnable tool, but for a researcher or engineer mapping a new field it saves real time compared to hunting through scattered sources.