Introduction to Transformer Models for NLP: Unit 2

About this course

This course covers the fundamentals and advanced applications of BERT and GPT models. You will learn how BERT processes text, including tokenization and vectorization, and practice fine-tuning BERT for tasks such as sequence classification, token classification, and question answering. The course also explains how GPT generates text, adapts to different writing styles, and can be fine-tuned for tasks like translating English to code. Additional topics include semantic search using Siamese BERT and multi-task learning with GPT through prompt engineering. By the end of the course, you will have the practical skills and theoretical understanding needed to apply BERT and GPT to various natural language processing problems.

What you will learn

• Master the architectures and core mechanisms of BERT and GPT for natural language understanding and generation.
• Fine-tune pre-trained models for advanced NLP tasks such as classification, question answering, and semantic search.
• Apply hands-on techniques to customize BERT and GPT for specific domains and writing styles.
• Utilize prompt engineering and few-shot learning to solve multiple NLP tasks efficiently.