Definition
Transfer learning is a machine learning technique where a model trained on one task is repurposed for a second, related task. Instead of building a model from scratch, you leverage the knowledge gained from a pre-existing model, significantly reducing training time and data requirements. This approach works by taking a trained model and fine-tuning its later layers on a new dataset specific to your problem. It matters on the job because it allows for faster development of accurate AI solutions, especially when dealing with limited data. For instance, a model trained to recognize general objects can be adapted to specifically identify different types of medical equipment. This differs from traditional model training which starts with random weights and learns everything from the ground up. You will encounter transfer learning concepts in AI focused certifications and cloud architecture exams like AWS SAA.