A from-scratch test of how learning-rate schedules are actually computed. The signal is correct closed-form formulas, knowing when each schedule shines, and why decaying the rate helps convergence. Here is the implementation of the common ones.
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Implement learning-rate schedulers from scratch: step decay, exponential decay, and cosine annealing.
A from-scratch test of how learning-rate schedules are actually computed. The signal is correct closed-form formulas, knowing when each schedule shines, and why decaying the rate helps convergence. Here is the implementation of the common ones.
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 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.Next in this trackBuild a mini data loader with sharding for distributed training: split data across workers without overlap.
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