lstm

Applied AI interview questions tagged lstm, across every topic.

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Concepts behind "lstm"

The curriculum that explains the ideas these questions test.

🧠 Foundations of LLMs & GenAI

From RNNs to Transformers: RNN, LSTM, Seq2SeqRecurrent networks process sequences one step at a time through a hidden state, which makes them principled but slow and bad at long-range dependencies because gradients vanish across many steps. LSTMs and GRUs add gates to carry information further, and seq2seq encoder-decoder models with attention removed the single-vector bottleneck, which is the idea transformers then took to its conclusion. Applied-AI interviews probe this because it explains why attention exists and why we abandoned recurrence for parallelism.

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