Hands-On Natural Language Processing with PyTorch 1.x

Text becomes more useful when you can represent it, learn from it, and build applications around it. In Hands-On Natural Language Processing with PyTorch 1.x, Thomas Dop takes readers from machine-learning foundations to practical NLP models, with examples that connect the mechanics of PyTorch to real language-processing tasks.

The progression is hands-on: first the tools and core ideas, then text preparation and representation, followed by neural-network applications such as sentiment analysis and classification. Readers should have working knowledge of Python and some familiarity with basic NLP tasks.

Start with PyTorch and the foundations of learning

The opening section introduces supervised and unsupervised learning, neural networks, activation functions, and overfitting. It then turns to PyTorch 1.x, tensors, CUDA acceleration, and building and training a simple classifier. That grounding helps make later model examples easier to follow.

See how text is represented and prepared

Chapters on NLP fundamentals explore word embeddings, GLoVe, CBOW, n-grams, tokenization, part-of-speech tagging, chunking, and TF-IDF. The book also covers practical text preprocessing, including case conversion, punctuation and HTML removal, stemming, and lemmatization. Together, these topics show how raw text can be transformed into forms that models can work with.

Put neural networks to work on language

Later chapters apply recurrent neural networks and LSTMs to sentiment analysis, then introduce convolutional neural networks for text classification. The examples move beyond model concepts into training, making predictions, and evaluating results.

From model to application

The book also follows an NLP example into application development, including creating a Flask API and deploying it on Heroku. This makes the material relevant to readers interested not only in how a model is built, but also in how it can be put into use.

A practical fit for Python developers exploring NLP

Developers working with language data, or machine-learning learners ready to apply neural networks to text, will find a structured introduction to NLP with PyTorch 1.x. The emphasis is on understanding core techniques through implementation—an approachable way to connect text-processing concepts with working models.