Transformer Models for NLP: Foundations of BERT and GPT

Natural Language Processing has been revolutionized by transformer architectures, fundamentally changing how machines comprehend and generate text. To work with modern artificial intelligence, understanding the core mechanics of BERT and GPT is an essential first step. This text-based course guides you from foundational NLP concepts to the inner workings of state-of-the-art transformer models. You will read clear explanations, analyze code snippets, and build a conceptual framework for working with modern language models. What you'll learn: - Understand the fundamental architecture of transformer models, including self-attention mechanisms. - Analyze how BERT processes text bidirectionally for comprehension tasks like classification and named entity recognition. - Explore how GPT architectures generate coherent text sequentially using autoregressive decoding. - Compare encoder-only, decoder-only, and encoder-decoder models to choose the right tool for your NLP tasks. - Apply modern prompt engineering concepts and understand the basics of Retrieval-Augmented Generation (RAG). The course begins with core definitions of tokens, embeddings, and attention layers before walking through step-by-step written explanations and conceptual comparisons of BERT and GPT. Designed for beginners in machine learning and software developers looking to transition into NLP, this program requires no prior deep learning experience. Start reading today to master the underlying technology powering modern generative AI.