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lstm

AI, ML & GenAI interview questions tagged lstm, across every topic.

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Concepts behind "lstm"

The curriculum that explains the ideas these questions test.

Foundational
🧠 Foundations of LLMs & GenAI
From RNNs to Transformers: RNN, LSTM, Seq2SeqRecurrent networks walk through a sequence one position at a time via a hidden state, an approach that is principled but slow and weak on long-range dependencies because gradients shrink across many steps. Gates in LSTMs and GRUs carry information further, and seq2seq encoder-decoder models with attention broke the single-vector bottleneck, the idea transformers later pushed all the way. AI, ML, and GenAI engineer interviews probe this because it explains where attention came from and why the field traded recurrence for parallelism.
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