Deep Learning For Dummies by John Paul Mueller & Luca Massaron

Author:John Paul Mueller & Luca Massaron [Mueller, John Paul & Massaron, Luca]
Language: eng
Format: epub
ISBN: 9781119543039
Publisher: Wiley
Published: 2019-05-14T00:00:00+00:00

Transferring learning

Flexibility is handy even when a network completes its training, but you must reuse it for purposes different from the initial learning. Networks that distinguish objects and correctly classify them require a long time and a lot of computational capacity to learn what to do. Extending a network’s capability to new kinds of images that weren’t part of the previous learning means transferring the knowledge to this new problem (transfer learning).

For instance, you can transfer a network that’s capable of distinguishing between dogs and cats to perform a job that involves spotting dishes of macaroni and cheese. You use the majority of the layers of the network as they are (you freeze them) and then work on the final, output layers (fine-tuning). In a short time, and with fewer examples, the network will apply what it learned in distinguishing dogs and cats to macaroni and cheese. It will perform even better than a neural network trained only to recognize macaroni and cheese.

Transfer learning is something new to most machine learning algorithms and opens up a possible market for transferring knowledge from one application to another, from one company to another. Google is already doing that, actually sharing its immense data repository by making public the networks that it built on it (as detailed in this post: https://techcrunch.com/2017/06/16/object-detection-api/). This is a step in democratizing deep learning by allowing everyone to access its potentiality.

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