DeepLearningForNLPInPytorch

Why we included this project

This tutorial reads like a well-structured course rather than a collection of scattered code samples. It starts with tensors and computation graphs, then walks through word embeddings and sequence modeling before tackling BiLSTM-CRFs for structure prediction, with working notebooks for an LSTM part-of-speech tagger and a named-entity recognizer along the way. Because it was written for PyTorch's dynamic computation graphs, it's a good fit for developers coming from static-graph frameworks who want to see how dynamic execution changes NLP model design. It was created early in PyTorch's history, so some APIs have aged, but the conceptual grounding in classic sequence-labeling tasks remains a practical study path for coursework or interview prep.