What you will learn

• Explain the role of attention mechanisms in transformer models for capturing contextual relationships in text

• Describe the differences in language modeling approaches between decoder-based models like GPT and encoder-based models like BERT

• Implement key components of transformer models, including positional encoding, attention mechanisms, and masking, using PyTorch

• Apply transformer-based models for real-world NLP tasks, such as text classification and language translation, using PyTorch and Hugging Face tools