Transfer Learning
A foundational ML concept where a model developed for one specific task is reused as the starting point for a model on a second related task.
Think of It Like This
Like a professional tennis player using their deeply ingrained hand-eye coordination and footwork to quickly master playing badminton.
Transfer learning circumvents the need to train massive neural networks from scratch. For example, a vision model trained to identify 1,000 different objects (like ImageNet) already understands edges, textures, and shapes. This model can be trivially fine-tuned on a tiny dataset of medical X-rays to detect cancer with incredible accuracy.