Understand the fundamental architecture and mechanisms behind Transformer models. This course covers attention mechanisms, encoder-decoder structures, and the principles enabling state-of-the-art results in natural language processing.
Prerequisites ML & Python Basics
Level:
Attention Mechanisms
Explain the concept of attention and differentiate between various attention mechanisms.
Self-Attention
Describe how self-attention allows models to weigh the importance of different words in a sequence.
Transformer Architecture
Outline the components of the Transformer model, including encoder and decoder stacks.
Multi-Head Attention
Understand the rationale and implementation of multi-head attention.
Positional Encoding
Explain the necessity and methods for incorporating sequence order information.
Basic Implementation
Implement core components of the Transformer architecture using a deep learning framework.