Natural Language Processing with PyTorch and fastai

Understanding how computers process human language is one of the most valuable skills in modern software development. This text-based course guides you through the core concepts of Natural Language Processing (NLP) using PyTorch and fastai, transforming theoretical deep learning into practical, readable code. You will start with the absolute fundamentals of text representation before moving on to training powerful language models. By working through written explanations and clear code examples, you will learn how to prepare text data, build neural networks, and fine-tune pre-trained models for your specific needs. We also cover modern developments in the field, including transformer architectures and prompt engineering foundations, ensuring your skills remain current. What you'll learn: - Understand the core concepts of tokenization, numericalization, and word embeddings - Build and train text classification models using PyTorch and fastai - Apply transfer learning techniques to adapt large language models for custom tasks - Implement modern transformer-based architectures for sequence classification - Clean, preprocess, and structure raw text datasets for deep learning pipelines - Evaluate model performance using standard NLP metrics and validation strategies This course begins with foundational vocabulary and basic text processing before stepping into neural network architectures and model optimization. Through structured text lessons and code snippets, you will build a solid intuitive understanding of how deep learning models read and generate text. This course is designed for software developers, data analysts, and curious programmers who have basic familiarity with Python but are new to deep learning and NLP. No advanced mathematics or machine learning background is required. Dive into the text and start building your first language models today.