Transfer Learning in Action: From ImageNet to Tiny-ImageNet

Transfer Learning in Action: From ImageNet to Tiny-ImageNet

Transfer learning is an important topic. As a civilization, we have been passing on the knowledge from one generation to the other, enabling the technological advancement that we enjoy today. It’s the edifice that supports most of the state-of-the-art models that are blowing steam, empowering many services that we take for granted. > Transfer learning is about having a good starting point for the downstream task we’re interested in solving. In this article, we’re going to discuss how to piggyback on transfer learning to get a **_warm start_** to solve an image classification task. The content of this article is based on [“TensorFlow 2 in Action” by Manning](https://www.manning.com/books/tensorflow-in-action?utm_source=thushv&utm_medium=affiliate&utm_campaign=book_ganegedara_tensorflow_10_13_20&a_aid=thushv&a_bid=a9e673f5) and on TensorFlow 2.2.

Transfer learning is an important topic. As a civilization, we have been passing on the knowledge from one generation to the other, enabling the technological advancement that we enjoy today. It’s the edifice that supports most of the state-of-the-art models that are blowing steam, empowering many services that we take for granted.

Transfer learning is about having a good starting point for the downstream task we’re interested in solving.

In this article, we’re going to discuss how to piggyback on transfer learning to get a warm start to solve an image classification task. The content of this article is based on “TensorFlow 2 in Action” by Manning and on TensorFlow 2.2.

deep-learning machine-learning artificial-intelligence tensorflow

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