long short-term memory (lstm) layers
Long Short-Term Memory (LSTM) layers are a type of artificial recurrent neural network (RNN) layer commonly used in deep learning models. They are designed to effectively process and capture long-term dependencies in sequential data by using a sophisticated gating mechanism. LSTM layers allow the network to retain and update information over time, making them particularly useful for tasks involving sequential data such as natural language processing, speech recognition, and time series prediction.
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Related Concepts (16)
- artificial neural networks
- deep learning
- financial market prediction
- generative modeling
- gesture recognition
- image captioning
- machine translation
- music generation
- natural language processing
- neural network layers
- recurrent neural networks (rnn)
- sentiment analysis
- sequential data analysis
- speech recognition
- time series prediction
- video activity recognition
Similar Concepts
- attention layers
- autoencoder layers
- convolutional layers
- convolutional neural networks (cnn)
- dropout layers
- embedding layers
- fully connected layers
- long short-term memory (lstm)
- long-term memory
- memory attention networks
- memory circuits
- memory layout
- multilayer perceptrons
- recurrent layers
- short-term memory