Transformer deep learning architecture

  1. Architectural Analysis
    1. Computational Efficiency
      1. Parallelization Benefits
        1. Attention Parallelism
          1. Layer Parallelism
            1. Sequence Parallelism
            2. Hardware Utilization
              1. GPU Acceleration
                1. Memory Bandwidth
                  1. Computational Throughput
                2. Scalability Properties
                  1. Model Size Scaling
                    1. Parameter Count Growth
                      1. Computational Requirements
                      2. Sequence Length Scaling
                        1. Quadratic Attention Complexity
                          1. Memory Scaling
                          2. Training Data Scaling
                          3. Representational Capacity
                            1. Universal Approximation
                              1. Expressiveness
                                1. Inductive Biases
                                  1. Lack of Sequential Bias
                                    1. Attention-based Bias
                                  2. Limitations and Challenges
                                    1. Quadratic Complexity
                                      1. Long Sequence Challenges
                                        1. Memory Constraints
                                        2. Position Encoding Limitations
                                          1. Fixed Maximum Length
                                            1. Extrapolation Challenges
                                            2. Training Requirements
                                              1. Large Data Needs
                                                1. Computational Resources
                                                  1. Hyperparameter Sensitivity

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                                              7. Mathematical Foundations

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                                              9. Interpretability and Analysis