Transformer by hand

• The video provides a step-by-step walkthrough of the Transformer architecture, comparing it to opening the engine hood of a car.
• The process begins with features from the previous block as input.
• Query (Q) and Key (K) matrices are derived from the input features for attention calculation.
• An Attention Weight Matrix (A) is computed using the Q and K values.
• The attention weights are applied to Value (V) vectors to produce attention-weighted features.
• The output sequence Z1 through Z5 represents the result of the self-attention mechanism.
• The weighted features pass through a Position-wise Feed-Forward Network (FFN).
• The FFN includes a ReLU activation function to introduce non-linearity.
• The final output of the block is passed to the next block in the stack.
• The creator identifies the series as 'AI by Hand' by Prof. Tom Yeh.

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