Transfer learning is a machine learning technique where a model trained on one task is adapted for a different but related task. It allows leveraging knowledge gained from one domain to improve performance in another. Here are 100 tips for working with transfer learning:
1. Understanding Transfer Learning:
- Conceptual Understanding: Grasp the fundamental concepts of transfer learning and its applications.
- Types of Transfer Learning: Differentiate between types of transfer learning: feature extraction and fine-tuning.
2. Data Preparation:
- Data Exploration: Explore the target domain data to understand its characteristics and potential challenges.
- Data Augmentation: Apply data augmentation techniques to increase the diversity of the target domain data.
3. Model Selection:
- Pre-trained Models: Choose pre-trained models that are well-suited for the task at hand.
- Model Compatibility: Ensure the pre-trained model is compatible with the target domain and task.
