nlp_overview

Why we included this project

Modern NLP research is scattered across papers, tutorials, and code, and it is easy to lose the thread of how the deep learning methods fit together. This project collects that material into one structured walkthrough, starting with distributed representations like word2vec and contextual embeddings and continuing through CNNs, RNNs, and reinforcement learning, with applications to machine translation, question answering, and dialogue systems. Each topic pairs the theory with implementation details, and the companion nlpoverview.com site tracks state-of-the-art results and new benchmark datasets as they appear. It works well as an orientation for a graduate student, a researcher moving into NLP, or a practitioner who wants conceptual grounding before picking a deep learning approach to try. Think of it as a well-organized syllabus rather than a library you import into your project.