Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second task. This approach leverages the knowledge gained from the initial task to improve learning efficiency and performance on the new, related task.
How Transfer Learning Works:
- Pre-training: A model is trained on a large dataset for a task with abundant data.
- Fine-tuning: The pre-trained model is adapted to a new, related task by training it on a smaller dataset specific to that task.
This process allows the model to apply previously learned features to new problems, reducing the need for extensive data and computational resources.
Applications of Transfer Learning:
- Image Classification: Models trained on large image datasets can be fine-tuned for specific tasks like medical image analysis or facial recognition.
- Natural Language Processing (NLP): Pre-trained language models can be adapted for tasks such as sentiment analysis, translation, or text summarization.
- Speech Recognition: Models trained on general speech data can be fine-tuned for specific accents or languages.