Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data with connections between nodes forming cycles.
In-depth explanation
RNNs 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
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Convolutional Neural Network (CNN)
A neural network architecture designed for processing grid-like data such as images.
LSTM (Long Short-Term Memory)
An RNN variant with gates that control information flow, enabling learning of long-term dependencies.
Transformer
A neural network architecture based on self-attention mechanisms, powering modern language models.
Attention Mechanism
A technique that allows models to focus on relevant parts of the input when producing output.
Transfer Learning
Using knowledge learned from one task to improve performance on a different but related task.
Fine-Tuning
Adapting a pre-trained model to a new task by training on task-specific data.
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