Generative AI Language Modeling with Transformers
Completed by Ahmed Mohamed Mohamed Atwan
August 25, 2026
9 hours (approximately)
Ahmed Mohamed Mohamed Atwan'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: Model Training
- Category: Natural Language Processing
- Category: Large Language Modeling
- Category: Generative Model Architectures
- Category: Generative AI
- Category: Applied Machine Learning
- Category: Data Preprocessing
- Category: Model Optimization
- Category: Embeddings
- Category: Transfer Learning
- Category: PyTorch (Machine Learning Library)
