Generative AI Language Modeling with Transformers
Completed by Giuseppe Ruggeri
August 24, 2024
9 hours (approximately)
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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: Data Preprocessing
- Category: Embeddings
- Category: Transfer Learning
- Category: PyTorch (Machine Learning Library)
- Category: Model Optimization
- Category: Large Language Modeling
- Category: Natural Language Processing
- Category: Applied Machine Learning
- Category: Generative AI
- Category: Generative Model Architectures
- Category: Model Training
