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Long Short Term Memory Networks

Long Short-Term Memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to learn order dependence in sequence prediction problems. They are particularly effective in handling complex tasks such as machine translation and speech recognition, where understanding the context and sequence of data is crucial ⁴.

LSTMs address common issues faced by traditional RNNs, such as vanishing and exploding gradients, which can hinder learning in long sequences. By utilizing a unique architecture that includes memory cells and gates, LSTMs can maintain information over extended periods, making them suitable for tasks that require remembering previous inputs ².

Additionally, there are variations of LSTMs, such as CNN LSTMs, which combine convolutional neural networks with LSTMs to process spatial data like images or videos effectively. This architecture is particularly useful for sequence prediction problems involving spatial inputs ³.

If you have more specific questions about LSTMs or their applications, feel free to ask!

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