Transformer Models and BERT: NLP Foundations

This course provides an in-depth introduction to Transformer models and the BERT (Bidirectional Encoder Representations from Transformers) model, which are at the forefront of natural language processing (NLP) technology. The curriculum focuses on the core components of the Transformer architecture, with particular emphasis on the self-attention mechanism. Students will learn how these elements come together to form the BERT model and explore its applications in various NLP tasks. The course covers advanced topics such as text classification, question answering, and natural language inference, demonstrating BERT's versatility in handling complex language understanding tasks. Through a combination of theoretical explanations and practical insights, learners will gain a solid understanding of these state-of-the-art NLP models and their potential applications in real-world scenarios.