nlp

Why we included this project

For developers learning NLP, this repo reads like one engineer's notebook made public: each concept, from tokenization and part-of-speech tagging to subword embeddings and data augmentation, comes with a runnable Jupyter notebook and a blog post explaining the reasoning behind it. The author walks through building real text pipelines step by step rather than collecting theory, and the sections on adversarial attacks, data noising, and back translation cover practical ground that introductory material usually skips. Newcomers can use it as a self-study map, while working practitioners can jump in for a quick refresher on a specific preprocessing technique.