Transformer Models for NLP: A Beginner's Guide to BERT and GPT

Natural language processing has been revolutionized by transformer models, which power everything from search engines to conversational AI. Understanding these architectures is essential for anyone entering the field of modern language technology. In this written course, you will transition from learning basic NLP concepts to understanding how state-of-the-art transformer models function under the hood. What you'll learn: - Understand foundational NLP concepts and the historical architectures that led to transformers - Learn the mechanics of the self-attention mechanism and transformer encoder-decoder structures - Explore the differences between masked language models like BERT and autoregressive models like GPT - Practice drafting effective prompts and understand basic prompt engineering principles - Discover how modern retrieval-augmented generation (RAG) systems leverage these models - Comprehend the process of fine-tuning pre-trained models for specific text-processing tasks The course starts with essential terminology and traditional NLP techniques before detailing the transformer architecture. You will then explore practical applications, including modern language generation and text classification workflows. This course is designed for beginners, software developers, and data enthusiasts looking for a clear, conceptual introduction to modern NLP without requiring advanced mathematical prerequisites. Start reading today to build a strong foundation in the technology driving the AI revolution.