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How to use Deep Learning when you have Limited Data
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.
nanonets.ai help builds Machine Learning with fewer data.
A few examples of number of parameters in these recent models are: