1. Pre-training: A model is trained on a large dataset (e.g., millions of images).
2. Knowledge Transfer: The trained model is used as a base for a new, related task.
3. Fine-tuning: The model is slightly modified and trained with a smaller dataset specific to the new task.

This allows AI to learn faster and perform better without needing massive amounts of data.

Uses:

• Image Recognition – AI trained on millions of photos can be adapted to recognize medical scans or faces.
• Speech Recognition – AI models like Google Assistant and Alexa use Transfer Learning to understand different languages.
• Text Processing (NLP) – AI trained for English text can be fine-tuned to understand other languages or specific topics.
• Self-Driving Cars – AI trained on simulated driving can be adapted to real-world roads.