Natural Language Processing with PyTorch

Natural Language Processing is at the core of modern intelligent applications, driving everything from sentiment analysis to automated translation. This course provides a structured introduction to processing and analyzing textual data using PyTorch. You will build a strong foundation in text processing techniques, progressing from classical language representations to modern deep learning architectures. Through written explanations and code snippets, you will learn how to design, train, and evaluate neural models for real-world language tasks. What you will learn: Understand key NLP terminology, tokenization, and vector space representations. Build recurrent networks and sequence-to-sequence models in PyTorch. Apply fine-tuning techniques using modern pretrained transformer models. Explore essential concepts in Retrieval-Augmented Generation and prompt engineering. Design and evaluate text classification and sentiment analysis workflows. Solve practical text processing problems through written exercises. The course begins with foundational concepts and core mathematical definitions before guiding you through sequence models and modern transformer architectures. It is designed for beginners interested in deep learning for text, requiring only basic Python knowledge. Start reading today to build practical NLP skills with PyTorch.