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Implement a vanilla RNN cell from scratch: forward over a sequence and backprop through time.

A from-scratch test of recurrent forward passes and backprop through time. The signal is the shared-weight recurrence, accumulating gradients across timesteps, and explaining the vanishing-gradient problem. Here is the implementation.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

A from-scratch test of recurrent forward passes and backprop through time. The signal is the shared-weight recurrence, accumulating gradients across timesteps, and explaining the vanishing-gradient problem. Here is the implementation.

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UP NEXT ON YOUR JOURNEY
Next in this trackImplement an LSTM cell from scratch: the four gates, the cell state, and why it beats a vanilla RNN.Next in this trackImplement the AdamW optimizer from scratch and explain how decoupled weight decay differs from Adam.Next in this trackImplement learning-rate schedulers from scratch: step decay, exponential decay, and cosine annealing.
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