Fine-Tuning
Adapting a pre-trained model to a new task by training on task-specific data.
In-depth explanation
Fine-tuning takes a model pre-trained on a large dataset and continues training on a smaller, task-specific dataset. This can involve updating all weights or only certain layers. Learning rate is usually lower than initial training to avoid destroying pre-trained knowledge. Fine-tuning is a key technique in modern NLP and computer vision.
Examples
Related terms
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.
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.
Transfer Learning
Using knowledge learned from one task to improve performance on a different but related task.
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