AI GlossaryRecurrent Neural Network (RNN)ENTRY — Deep Learning
[Deep Learning]
Recurrent Neural Network (RNN).
A neural network architecture designed for sequential data with connections between nodes forming cycles.
In-depth explanation
01RNNs process sequences by maintaining a hidden state that captures information from previous time steps. This memory enables them to handle variable-length sequences and capture temporal dependencies. However, basic RNNs struggle with long sequences due to vanishing gradients. LSTM and GRU variants address this limitation.
Examples
02EX. 01
Language modeling
EX. 02
Speech recognition
EX. 03
Time series prediction
More in Deep Learning
0301Attention MechanismA technique that allows models to focus on relevant parts of the input when producing output.02Convolutional Neural Network (CNN)A neural network architecture designed for processing grid-like data such as images.03DropoutA regularization technique that randomly drops neurons during training to prevent overfitting.04Fine-TuningAdapting a pre-trained model to a new task by training on task-specific data.05LSTM (Long Short-Term Memory)An RNN variant with gates that control information flow, enabling learning of long-term dependencies.06Transfer LearningUsing knowledge learned from one task to improve performance on a different but related task.
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