Attached is an algorithm that does real-time deep learning.
The “observations” input is the dataset. The observations dataset is meant to simulate a buffer, and as data is read from it, that data is used to make predictions, and build a training dataset, from which predictions are generated. However, once the accuracy of the predictions meets or exceeds the value of “threshold”, the algorithm stops learning and only makes predictions. If the accuracy drops below threshold, then learning begins again. “N” is the dimension of the dataset, above which data is ignored.
Also attached is a command line script demonstrating how to use the algorithm.
Finally, here’s related code that does real-time video classifications:
The image files for the video training example can be found on dropbox.