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Long Short Term Memory

Long Short-Term Memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to effectively learn from sequences of data. They address the limitations of traditional RNNs, particularly the issues of vanishing and exploding gradients, which can hinder learning in long sequences. LSTMs are capable of capturing long-range dependencies in data, making them suitable for tasks such as machine translation, speech recognition, and time series prediction ¹⁴.

The architecture of LSTMs includes memory cells that can maintain information over long periods, along with gates that control the flow of information into and out of these cells. This allows LSTMs to learn which information is relevant to keep or discard, enhancing their performance on complex sequence prediction problems ³⁴.

In addition to standard LSTMs, there are variations like CNN LSTMs, which combine convolutional neural networks with LSTMs to handle spatial data, such as images or videos, effectively ².

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

A basic introduction to Long Short-Term Memory Networks

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In the previous article of the series, we talked about the drawbacks of RNNs which constitutes vanishing and exploding gradients. These can be overcome by Long short-term memory (LSTM) networks. LSTM…...

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Understanding Long-Short Term Memory

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By Abdul Rauf Jatoi Intro In the world of machine learning and artificial intelligence, understanding how to deal with sequences of data is crucial. Sequences are everywhere — in speech, video, stock...

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Last Updated on August 14, 2019 Gentle introduction to the Encoder-Decoder LSTMs for sequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network de...

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Long Short Term Memory networks — usually just called “LSTMs” — are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997) and were…...