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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 effectively learn and remember long-term dependencies in sequential data. Introduced by Hochreiter and Schmidhuber in 1997, LSTMs address the limitations of traditional RNNs, such as the vanishing and exploding gradient problems, which hinder their ability to retain information over extended sequences. By incorporating memory cells and gating mechanisms, LSTMs can selectively remember or forget information, making them particularly well-suited for tasks like time series forecasting, natural language processing, and speech recognition. Their ability to capture context over time has made them a popular choice in various applications.
Long Short-Term Memory Networks
Introduction Neural networks are designed to mimic the behavior of human brains, to understand various relationships in the data. These networks have the power to understand complex non-linear relati...
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LSTM- Long Short-Term Memory
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…...
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A basic introduction to Long Short-Term Memory Networks
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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On the Suitability of Long Short-Term Memory Networks for Time Series Forecasting
Last Updated on August 5, 2019 Long Short-Term Memory (LSTM) is a type of recurrent neural network that can learn the order dependence between items in a sequence. LSTMs have the promise of being able...
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Demonstration of Memory with a Long Short-Term Memory Network in Python
Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning over long sequences. This differentiates them from regular multilayer ...
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Gentle Introduction to Generative Long Short-Term Memory Networks
Last Updated on August 14, 2019 The Long Short-Term Memory recurrent neural network was developed for sequence prediction. In addition to sequence prediction problems. LSTMs can also be used as a gene...
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CNN Long Short-Term Memory Networks
Last Updated on August 14, 2019 Gentle introduction to CNN LSTM recurrent neural networks with example Python code. Input with spatial structure, like images, cannot be modeled easily with the standar...
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Long Short-Term Memory (LSTM)
Shortly after the first Elman-style RNNs were trained using backpropagation ( Elman, 1990 ) , the problems of learning long-term dependencies (owing to vanishing and exploding gradients) became salien...
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Long Short-Term Memory (LSTM)
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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Long Short Term Memory (LSTM)— Improving RNNs
In this article, we will introduce Long-Short-Term Memory Networks (LSTMs), variants of regular vanilla Recurrent Neural Networks (RNNs) that are better at dealing with long-term dependencies. They us...
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Stacked Long Short-Term Memory Networks
Last Updated on August 14, 2019 Gentle introduction to the Stacked LSTM with example code in Python. The original LSTM model is comprised of a single hidden LSTM layer followed by a standard feedforwa...
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Encoder-Decoder Long Short-Term Memory Networks
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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