What is transfer learning used for?#
This technique drastically reduces development time and cost by leveraging the knowledge from a pre-trained model, allowing businesses to create powerful, specialised AI solutions with much smaller, company-specific datasets.
How does transfer learning work?#
The process begins with a large "foundation model" that has already been trained on a massive, general dataset, such as millions of internet images or texts. This initial training has already taught the model to recognise fundamental patterns and concepts within its domain, like identifying shapes and objects in images or understanding grammar and context in language.
Instead of starting from zero, developers take this pre-trained model and fine-tune it using a much smaller, specific dataset relevant to the business need. This second training phase adjusts the model's existing knowledge to excel at the new, specialised task, such as identifying defects in a company's specific products or understanding industry-specific terminology for a customer service chatbot.
How is transfer learning different from training from scratch?#
Transfer learning customizes an existing, powerful AI model for a specific purpose. This is fundamentally different from training a model from scratch, which requires building and teaching a model from zero using a massive dataset. By starting with a pre-trained foundation, transfer learning allows for faster development with far less data and expense.