Understanding Add & Norm in Transformer Models

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This presentation delves into the Add & Norm function's crucial role within transformer models. By analyzing the importance of residual connections and layer normalization, we explore how these components address the vanishing gradient problem, ensuring information flow and enabling deeper model training. Further, the function's ability to standardize input and incorporate learnable parameters like gamma (γ) and beta (β) is discussed, highlighting its role in stabilizing and enhancing model...