Build NLP Applications with Deep Learning and Transformers

For ML engineers, data scientists and developers who write Python. Turn raw customer text into working NLP features: clean and tokenise it, build a TF-IDF baseline, learn how embeddings, recurrent networks and attention work, then fine-tune a pretrained transformer encoder for intent, sentiment, spam and named entities. Compare it honestly against a prompted large language model, add summarisation and question answering, and serve the model behind a FastAPI endpoint you can speed up and monitor. PyTorch and Hugging Face throughout, because Transformers v5 is PyTorch only. Finish with a free hands-on lab that builds and compares three message routers.