A from-scratch test of modern optimizers. The signal is the moment estimates, bias correction, and the one change that matters: AdamW decouples weight decay from the gradient instead of folding it into the loss. Here is the implementation and why it wins.
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Implement the AdamW optimizer from scratch and explain how decoupled weight decay differs from Adam.
A from-scratch test of modern optimizers. The signal is the moment estimates, bias correction, and the one change that matters: AdamW decouples weight decay from the gradient instead of folding it into the loss. Here is the implementation and why it wins.
Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.
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Next in this trackImplement learning-rate schedulers from scratch: step decay, exponential decay, and cosine annealing.Next in this trackBuild a tiny autograd engine from scratch: a scalar Value with backprop over a computation graph.Next in this trackImplement a WordPiece tokenizer from scratch: greedy longest-match-first subword segmentation.
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