Overview
Transfer learning is a technique in deep learning where a model developed for one task is reused for another related task. This approach helps save time and resources by leveraging existing knowledge. Instead of training a model from scratch, you can fine-tune a pre-trained model on a new dataset. This is especially useful in scenarios with limited data or computational power, making deep learning more accessible and efficient.
📚 Key Learning Objectives
- ✓ Explain the concept of transfer learning.
- ✓ Identify scenarios where transfer learning is beneficial.
- ✓ Differentiate between fine-tuning and feature extraction.
- ✓ Implement transfer learning using a pre-trained model.
- ✓ Evaluate the performance of a transfer learning model.
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