What is Transfer Learning?
Transfer learning is the practice of taking an AI model that has already learned general patterns from a massive dataset and adapting it to perform a new, specific task using a much smaller dataset.
How does it work?
Instead of building a neural network from scratch, developers start with a pre-trained model that already understands language, grammar, and basic facts. They then apply transfer learning (often via a process called fine-tuning) by training the model further on highly specific data, such as medical records or legal contracts.
What is a simple example?
If you want to learn to play the electric guitar, you will learn much faster if you already know how to play the acoustic guitar. The general knowledge of chords and rhythm transfers over, so you only need to learn the specific differences.
Why does it matter?
Training a foundation model from scratch costs millions of dollars and requires massive supercomputers. Transfer learning allows developers and researchers to create highly specialized, state-of-the-art AI systems cheaply and quickly by building on top of existing open-source models.