Transformer deep learning architecture

  1. Transformer Encoder
    1. Encoder Layer Architecture
      1. Sub-layer Organization
        1. Multi-Head Self-Attention
          1. Position-wise Feed-Forward Network
            1. Residual Connections
              1. Layer Normalization
              2. Information Processing Flow
              3. Multi-Head Self-Attention
                1. Self-Attention Mechanism
                  1. Query, Key, Value Concept
                    1. Linear Projections
                      1. Parameter Matrices
                      2. Attention Score Computation
                        1. Dot-product Calculation
                          1. Scaling Factor Application
                            1. Softmax Normalization
                            2. Weighted Value Aggregation
                              1. Mathematical Formulation
                              2. Multi-Head Extension
                                1. Attention Head Concept
                                  1. Parallel Attention Computations
                                    1. Different Representation Subspaces
                                    2. Head Implementation
                                      1. Dimension Splitting
                                        1. Independent Linear Projections
                                          1. Concatenation and Final Projection
                                          2. Benefits of Multiple Heads
                                            1. Diverse Attention Patterns
                                              1. Representation Richness
                                            2. Self-Attention Properties
                                              1. All-to-All Connections
                                                1. Parallel Computation
                                                  1. Long-Range Dependencies
                                                    1. Computational Complexity
                                                  2. Position-wise Feed-Forward Network
                                                    1. Architecture
                                                      1. Two Linear Transformations
                                                        1. ReLU Activation
                                                          1. Dimension Expansion and Contraction
                                                          2. Mathematical Formulation
                                                            1. Position Independence
                                                              1. Parameter Sharing Across Positions
                                                              2. Residual Connections and Normalization
                                                                1. Residual Connection Concept
                                                                  1. Skip Connections
                                                                    1. Gradient Flow Improvement
                                                                      1. Deep Network Training
                                                                      2. Layer Normalization
                                                                        1. Normalization Across Features
                                                                          1. Mean and Variance Computation
                                                                            1. Learnable Parameters
                                                                              1. Comparison with Batch Normalization
                                                                              2. Add & Norm Operations
                                                                                1. Operation Order
                                                                                  1. Pre-norm vs Post-norm
                                                                                2. Encoder Stack
                                                                                  1. Layer Stacking
                                                                                    1. Identical Layer Structure
                                                                                      1. Parameter Independence
                                                                                        1. Depth Considerations
                                                                                        2. Information Flow
                                                                                          1. Layer-by-layer Processing
                                                                                            1. Representation Refinement
                                                                                            2. Output Representation

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                                                                                          2. The Original Transformer Architecture

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                                                                                          4. Transformer Decoder