recurrent neural networks (rnn)
Recurrent neural networks (RNNs) are a type of artificial neural network designed to process sequential and time-series data. Unlike traditional feedforward neural networks, RNNs have feedback connections that allow them to maintain and utilize information from past inputs. This enables RNNs to effectively model dependencies and context within sequences, making them well-suited for tasks like speech recognition, language translation, text generation, and time-series prediction.
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Related Concepts (22)
- backpropagation through time (bptt)
- deep learning
- gated recurrent unit (gru)
- handwriting recognition
- image captioning
- language translation
- long short-term memory (lstm)
- long short-term memory (lstm) layers
- machine translation
- music generation
- named entity recognition
- natural language processing (nlp)
- question answering
- reinforcement learning
- sentiment analysis
- sequence modeling
- speech recognition
- stock market prediction
- text generation
- time series analysis
- vanishing gradient problem
- video analysis
Similar Concepts
- artificial neural networks
- convolutional neural networks
- convolutional neural networks (cnn)
- feedforward neural networks
- neural network architectures
- neural network layers
- neural network modeling
- neural network models
- neural network training
- neural network-based recommender systems
- neural networks
- recurrent layers
- recurrent neural networks
- recurrent neural networks with attention
- robustness of neural networks