Transformer
A neural network architecture based on self-attention mechanisms, powering modern language models.
In-depth explanation
Introduced in "Attention is All You Need" (2017), Transformers replaced RNNs for many sequence tasks. They use self-attention to weigh the importance of different input elements regardless of distance. This enables parallel processing and better handling of long-range dependencies. Transformers power GPT, BERT, and modern LLMs.
Examples
More in Deep Learning
Attention Mechanism
A technique that allows models to focus on relevant parts of the input when producing output.
Convolutional Neural Network (CNN)
A neural network architecture designed for processing grid-like data such as images.
Dropout
A regularization technique that randomly drops neurons during training to prevent overfitting.
Fine-Tuning
Adapting a pre-trained model to a new task by training on task-specific data.
LSTM (Long Short-Term Memory)
An RNN variant with gates that control information flow, enabling learning of long-term dependencies.
Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data with connections between nodes forming cycles.
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