Transfer Learning
Using knowledge learned from one task to improve performance on a different but related task.
In-depth explanation
Transfer learning leverages pre-trained models, typically trained on large datasets, as starting points for new tasks. Fine-tuning adapts the pre-trained weights to the target task. This approach dramatically reduces data and compute requirements for new tasks. It's especially powerful in computer vision and NLP where pre-training is expensive.
Examples
Related terms
More in Deep Learning
Convolutional Neural Network (CNN)
A neural network architecture designed for processing grid-like data such as images.
Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data with connections between nodes forming cycles.
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.
Fine-Tuning
Adapting a pre-trained model to a new task by training on task-specific data.
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