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

EX. 01

Using ImageNet-pretrained models

EX. 02

Fine-tuning BERT for sentiment

More in Deep Learning

01Attention 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.04Fine-TuningAdapting a pre-trained model to a new task by training on task-specific data.05LSTM (Long Short-Term Memory)An RNN variant with gates that control information flow, enabling learning of long-term dependencies.06Recurrent Neural Network (RNN)A neural network architecture designed for sequential data with connections between nodes forming cycles.

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