Transfer Learning
Transfer Learning is a machine learning approach where knowledge learned by a model on one task or dataset is reused to help train a model for a different but related task.
What is Transfer Learning?
Transfer learning typically starts with a pre-trained model that has already learned useful patterns from a large dataset. The model can then be fine-tuned or adapted using a smaller dataset for a specific task. This allows developers to reuse learned representations instead of training a model entirely from scratch.
Why is Transfer Learning Important?
Transfer learning can reduce training time, computational requirements, and the amount of task-specific data needed to build an effective model. It is particularly useful when large labeled datasets are unavailable or expensive to create.
Common use cases
Transfer Learning is commonly used in computer vision, natural language processing, speech recognition, image classification, sentiment analysis, and large language model development.