Generative AI Language Modeling with Transformers

This course provides a practical introduction to using transformer-based models for natural language processing (NLP) applications. You will learn to build and train models for text classification using encoder-based architectures like Bidirectional Encoder Representations from Transformers (BERT), and explore core concepts such as positional encoding, word embeddings, and attention mechanisms. The course covers multi-head attention, self-attention, and causal language modeling with GPT for tas

• 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