Applied Natural Language Processing Using PyTorch

This course aims to equip learners with the ability to execute advanced Natural Language Processing (NLP) tasks and develop intelligent language applications leveraging Deep Learning techniques via PyTorch.

Throughout the course, students will develop two comprehensive NLP projects: a Sentiment Analyzer designed to classify movie reviews as positive or negative, and an advanced Neural Translation Machine that employs Sequence-to-Sequence models for real-time speech translation across multiple languages, highlighting PyTorch’s speed and adaptability.

Upon completion, participants will possess the expertise to construct their own practical NLP models using PyTorch’s Deep Learning framework.

The accompanying source code is accessible on GitHub at: https://github.com/PacktPublishing/Hands-On-Natural-Language-Processing-with-Pytorch.

This curriculum utilizes Python 3.6, PyTorch 1.0, NLTK 3.3.0, and SpaCy 2.0, which, while not the most current versions, offer valuable insights for users working with legacy PyTorch environments.