Transfer Learning
Definition
Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and NLP tasks.
Why It Matters
Training large deep learning models from scratch requires massive amounts of data and computational power. Transfer learning allows developers to achieve high performance on their specific task with much less data by leveraging the knowledge already learned by a pre-trained model.
Contextual Example
A developer wants to build a model to classify different species of flowers. Instead of training a model from scratch, they start with a model like VGG16 that was pre-trained on the massive ImageNet dataset. They then "fine-tune" this model on their smaller dataset of flower images.
Common Misunderstandings
- Transfer learning significantly reduces training time and data requirements.
- It is one of the most important and practical techniques in modern deep learning.