AI GlossaryFine-TuningENTRY — Deep Learning
[Deep Learning]
Fine-Tuning.
Adapting a pre-trained model to a new task by training on task-specific data.
In-depth explanation
01Fine-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
02EX. 01
Fine-tuning GPT for chatbots
EX. 02
Fine-tuning BERT for classification
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.04LSTM (Long Short-Term Memory)An RNN variant with gates that control information flow, enabling learning of long-term dependencies.05Recurrent Neural Network (RNN)A neural network architecture designed for sequential data with connections between nodes forming cycles.06Transfer LearningUsing knowledge learned from one task to improve performance on a different but related task.
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