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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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Positional Encoding Explained: How Transformers Learn Word Order (Ep:04.04)
Attention is mathematically blind to sequence order — swap two tokens and every output is just the same values, permuted. Module 4 of From Zero to Agents derives sinusoidal positional…
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Learned Q, K, V Explained: What Makes Attention Actually Trainable (Ep:04.02)
Attention only becomes powerful once the query, key, and value projections are learned rather than reused from static embeddings. Module 4 of From Zero to Agents trains a real attention…
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Matrices — Organized, Simultaneous Dot Products (Ep:02.01)
A matrix is just organized, simultaneous dot products. Module 2 of From Zero to Agents builds matrix multiplication from first principles and uses it to fully decode the real attention…
