How to use Deep Learning when you have Limited Data

By on February 5, 2017
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One common barrier for using deep learning to solve problems is the amount of data needed to train a model. The requirement of large data arises because of a large number of parameters in the model that machines have to learn.

There are a lot of examples like Language Translation, playing Strategy Games and Self-Driving Cars which required millions of data. help builds Machine Learning with fewer data.

A few examples of number of parameters in these recent models are:

Recent advances in Deep Learning helped us build rich representations of data that are transferable across tasks. Using this technology we pre-train models on extremely large datasets that contain varied information. NanoNets are added to the existing model then trained on your data to solve your specific problem. Since NanoNets are smaller than traditional networks they require much less data and time to build.
Discover how NanoNets work:

About Boris Landoni

Boris Landoni is the technical manager of Skilled in the GSM field, embraces the Open Source philosophy and its projects are available to the community.

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