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Python Deep Learning - Second Edition
book

Python Deep Learning - Second Edition

by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
January 2019
Intermediate to advanced content levelIntermediate to advanced
386 pages
11h 13m
English
Packt Publishing
Content preview from Python Deep Learning - Second Edition

DL in the Cloud

In this chapter, we are discussing a serious topic, AVs and how to apply DL techniques in them. Let's see how to approach this task in practice. First, let's observe that in deep networks (as with most ML algorithms), we have two phases—training and inference. In most production environments, the network is trained once, and then used only in inference mode to solve tasks. If we obtain additional training data during the course of events, we can eventually train the network again (for example, using transfer learning). Then, we can embed the new model in the production environment until we need to retrain it again and so on. The alternative to this is incremental learning, having the model (network) constantly learn from new ...

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Publisher Resources

ISBN: 9781789348460Supplemental Content