Transformer deep learning architecture
Attention Parallelism
Layer Parallelism
Sequence Parallelism
GPU Acceleration
Memory Bandwidth
Computational Throughput
Parameter Count Growth
Computational Requirements
Quadratic Attention Complexity
Memory Scaling
Lack of Sequential Bias
Attention-based Bias
Long Sequence Challenges
Memory Constraints
Fixed Maximum Length
Extrapolation Challenges
Large Data Needs
Computational Resources
Hyperparameter Sensitivity
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7. Mathematical Foundations
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9. Interpretability and Analysis