Generative AI Language Modeling with Transformers
Completed by User 102008
January 25, 2026
9 hours (approximately)
User 102008's account is verified. Coursera certifies their successful completion of Generative AI Language Modeling with Transformers
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
Skills you will gain
- Category: Large Language Modeling
- Category: Natural Language Processing
- Category: Generative AI
- Category: Model Training
- Category: Generative Model Architectures
- Category: Data Preprocessing
- Category: Model Optimization
- Category: Embeddings
- Category: Transfer Learning
- Category: Applied Machine Learning
- Category: PyTorch (Machine Learning Library)
