Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

Recurrent neural networks

Not all data in the world exists independently of time. Stock market prices and spoken/written words are just a few examples of data that is bound to a time series. Therefore, the sequence of data has a temporal dimension, and you might assume that being able to use it in the manner befitting to data, which comes with the passage of time instead of a chunk of data that remains constant, would be more intuitive and would produce better prediction accuracy. In many cases, this has been found to be true and has led to the emergence of neural network architectures that can take time as a factor while learning and predicting. 

One such architecture is the recurrent neural network (RNN). The major characteristic of such ...