Hands-On Transfer Learning with Python

Advantages of transfer learning

We utilize knowledge from source models to improve learning in the target task. Apart from providing capabilities to reuse already-built models, transfer learning may assist learning the target task in the following ways:

• Improved baseline performance: When we augment the knowledge of an isolated learner (also known as an ignorant learner) with knowledge from a source model, the baseline performance might improve due to this knowledge transfer.
• Model-development time: Utilizing knowledge from a source model might also help in fully learning the target task, as compared to a target model that learns from scratch. This, in turn, results in improvements in the overall time taken to develop/learn a model.
• Improved ...