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How Attention Replaced Recurrence: The Full Story, From RNNs to a Working GPT
RNNs, LSTMs, attention, transformer blocks, positional encoding, causal masking — six ideas that each fixed a specific, provable failure in the one before it, ending in a complete working language…
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RNNs and the Vanishing Gradient Problem: Why Attention Had to Be Invented (Ep:04.00)
An RNN reuses the same weight matrix at every timestep, which means the vanishing gradient problem from Module 3 comes back — across time instead of depth, and often much…
