Awesome-Deep-Learning-Resources

Why we included this project

Guillaume Chevalier put this list together while learning deep learning himself, and he says he worked through every item before adding it, which makes the curation feel trustworthy rather than scraped. The sections follow how a learner actually moves through the field: trends and online classes first, then books, articles, practical libraries, datasets, math theory, and the landmark papers on RNNs, CNNs, and attention mechanisms. That organization makes it easy to come back to when a topic needs a refresher, and the papers and theory sections point to the foundations behind current practice. The list is released into the public domain, so teams can adapt it into their own onboarding material without worrying about licensing.