Introduction to Transformer Models for NLP: Unit 1

About this course

This course covers the development of natural language processing (NLP), starting with basic concepts and moving to modern transformer architectures. You will learn about attention mechanisms and their impact on language modeling, as well as the details of transformer models, including scaled dot product attention and multi-headed attention. The course includes practical exercises in transfer learning using pre-trained models such as BERT and GPT, with instruction on fine-tuning these models for specific NLP tasks in PyTorch. By the end, you will understand the theory behind current NLP models and gain practical experience in applying them to real-world problems.

What you will learn

• Understand the evolution of NLP architectures and the transformative impact of attention mechanisms.
• Analyze the structure and mathematical foundations of transformer models, including scaled dot product and multi-headed attention.
• Apply transfer learning techniques using pre-trained language models such as BERT and GPT.
• Gain practical experience with PyTorch to fine-tune NLP models for custom tasks.