We now delve into the most exciting aspect of transformers: the Encoder Block. This is where the popular Attention techniques come into play, enabling large language models to grasp better human…

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This post provides a detailed, step-by-step explanation of the Transformer Encoder Block using TensorFlow, focusing on the Multi-Head Attention mechanism. It covers the creation of Queries, Keys, and Values, the Scaled Dot-Product Attention mechanism, and the addition of residual connections and Layer Normalization. The final component, the Feed-Forward Network (FFN), is also detailed. Code examples in TensorFlow are provided throughout to illustrate key concepts.

Transformer from Scratch in TF Part 2: Encoder