Transfer Learning

Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second, related task. It leverages pre-trained models on large datasets to improve learning efficiency and performance on new tasks with limited data. This approach is widely used in deep learning, particularly in computer vision and natural language processing.

Also known as: TL, Knowledge Transfer, Model Reuse, Pre-trained Models, Fine-tuning

Why learn Transfer Learning?

Developers should use transfer learning when working with limited labeled data, as it reduces training time and computational resources while often achieving better accuracy than training from scratch. It is essential for tasks like image classification, object detection, and text analysis, where pre-trained models (e.g., ResNet, BERT) provide robust feature representations. This technique is crucial in real-world applications where data collection is expensive or time-consuming.

Learning Resources

• tutorial: Transfer Learning - TensorFlow Tutorial
• tutorial: A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
• tutorial: Transfer Learning - PyTorch Tutorial
• course: Transfer Learning - Coursera Course
• video: Transfer Learning Explained - YouTube Video