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Travis Harper

Machine Learning Engineer at Masterful AI

Getting the most out of Transfer Learning

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Travis Harper

It’s no secret that just about any deep learning computer vision task can be improved with transfer learning. Transfer learning is a method where a model developed for one task is reused as the starting point for a model for another task. It leads to the question: how can we best reuse weights gathered from existing models trained on very large datasets to improve the performance and cut down development time on a new model for a smaller target dataset? In this post we’ll compare several approaches, provide experimental results, and show you how to easily incorporate transfer learning into your model development.

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