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About LSTM Networks: Gated Memory for Sequence Learning

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Master how LSTMs keep gradients alive across long sequences: the cell state and constant error carousel, the three gates that regulate it, and why plain RNNs fail under backpropagation through time. Build up to GRUs, bidirectional and stacked variants, sequence-to-sequence models with attention, and a clear-eyed picture of where recurrent models still beat transformers in 2026 — streaming, low-latency, and long-context regimes where linear-time recurrence matters.

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What You'll Learn

Concepts:
Bidirectional LSTMs LSTM Output Gate LSTM Forget Gate Attention Mechanism Vanishing Gradient Problem Stacked LSTMs Recurrent Neural Networks Backpropagation Through Time LSTMs versus Transformers Sequence-to-Sequence Learning Gated Recurrent Units Constant Error Carousel LSTM Input Gate LSTM Cell State

What you will learn

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