Explain Transformer Architecture & Self-Attention (Interview Answer)

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Let's start with the big picture. The transformer is an architecture that processes all tokens in a sequence at once instead of step by step like older RNNs. Its core idea is self attention which lets the model understand how each word relates to every other word. A strong interview answer is the transformer uses an encoder decoder design built from repeated blocks that rely on self attention, feed forward layers, residual connections, and normalization. This structure learns long range context …

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