long short-term memory (lstm)
Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture that improves the ability of the network to remember long-term dependencies and make accurate predictions. It achieves this by introducing a memory cell that allows the network to retain and selectively forget information over varying time intervals, enabling it to effectively process and store important information for longer periods of time.
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Related Concepts (20)
- anomaly detection
- automatic speech recognition (asr)
- deep learning
- emotion recognition
- gated recurrent units (gru)
- handwriting recognition
- image captioning
- language modeling
- machine translation
- named entity recognition (ner)
- natural language processing (nlp)
- object detection
- recurrent neural networks (rnn)
- reinforcement learning
- sentiment analysis
- sequence modeling
- speech recognition
- text generation
- time series prediction
- video analysis
Similar Concepts
- learning and memory
- long short-term memory (lstm) layers
- long-term memory
- memory and learning
- memory attention networks
- memory circuits
- memory development
- memory layout
- memory loss
- memory recall
- memory retrieval
- memory retrieval and reconstruction
- short-term memory
- working memory
- working memory capacity