Transfer Learning

Transfer learning is a technique in artificial intelligence and machine learning where a model developed for one task is reused as the starting point for a new, related task. Instead of training a model from scratch, transfer learning leverages the knowledge the model has already learned—such as patterns, features, or weights—on a large, general dataset, and fine-tunes it for a more specific or smaller-scale task.

Why It's Useful:

Training deep learning models from scratch often requires:

• Massive labeled datasets
• High computational power
• Long training times

Benefits:

• Saves time and resources
• Improves performance on small datasets
• Speeds up development cycles
• Enables rapid prototyping

Common Applications:

• Computer Vision: Using a model trained on general image recognition (e.g., ImageNet) and fine-tuning it for detecting diseases in X-rays or identifying plant species.
• Natural Language Processing (NLP): Adapting pretrained language models like BERT or GPT for tasks like sentiment analysis, question answering, or translation.
• Speech Recognition: Using a model trained on broad speech patterns and adapting it to a specific accent or language.

‍