Transfer learning is a machine learning technique that involves reusing a pre-trained model on a new problem.
It’s a popular approach in deep learning because it can train deep neural networks with comparatively little data.
This is very useful in the data science field since most real-world problems typically do not have millions of labeled data points to train such complex models.
In transfer learning, the knowledge of an already trained machine learning model is applied to a different but related problem. For example, if you trained a simple classifier to predict whether an image contains a backpack, you could use the knowledge that the model gained during its training to recognize other objects like sunglasses.
Applications for transfer learning
Transfer learning has numerous applications in the field of machine learning, including:
- Text and Image Classification: Transfer learning can be used to train models for text and image classification tasks.
- Training Self-Driving Vehicles: Transfer learning can be used to train self-driving vehicles using simulations. The pre-trained models can be used to detect objects on the road, such as pedestrians, cars, and traffic signs.
- Robot Training: Transfer learning can be used to train robots to perform various tasks, such as grasping objects, navigating environments, and more.
- Medical Image Analysis: Transfer learning can be used to analyze medical images, such as X-rays, CT scans, and MRIs. Pre-trained models can be used to detect abnormalities in the images, such as tumors, fractures, and more.
- AI Games: Transfer learning can be used to train AI agents to play games, such as chess, Go, and more. Pre-trained models can be used to teach the agents how to play the game and make decisions.