From-GANs-to-RAG-A-Journey-Through-Modern-Deep-Learning
A curated collection of foundational papers tracing the evolution from early generative models to modern retrieval-augmented language systems.
This repository presents a carefully structured reading path through 14 landmark papers that tell the story of modern deep learning. Starting with Generative Adversarial Networks and culminating in efficient Retrieval-Augmented Generation systems, this collection shows how each breakthrough built upon the previous one to shape today's AI landscape.
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