An in-depth examination of the Transformer architecture for experienced AI engineers. This course covers the theoretical underpinnings, mathematical details, and advanced implementation techniques behind modern large language models. Gain a sophisticated understanding of self-attention mechanisms, positional encodings, normalization layers, and architectural variants.
Prerequisites Deep Learning & Python Proficiency
Level:
Self-Attention Mechanisms
Analyze the mathematical formulation and computational aspects of scaled dot-product attention.
Multi-Head Attention
Understand the rationale and implementation details of projecting queries, keys, and values into multiple subspaces.
Positional Encoding
Evaluate different methods for injecting sequence order information into the Transformer model.
Encoder-Decoder Stack
Dissect the complete Transformer architecture, including layer normalization and feed-forward sub-layers.
Architectural Variants
Compare and contrast different Transformer modifications (e.g., sparse attention, linear transformers).
Implementation Considerations
Implement core Transformer components and understand computational efficiency trade-offs.
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