Building Recurrent Neural Networks in Tensorflow

Recurrent Neural Nets (RNN) detect features in sequential data (e.g. Examples of applications which can be made using RNN's are anomaly detection in time-series data, classification of ECG and EEG data, stock market prediction, speech recogniton, sentiment analysis, etc. This is done by unrolling the data into N different copies of itself (if the data consists of N time-steps) . In this way, the input data at the previous time steps can be used when the data at timestep is evaluated. If the data at the previous time steps is somehow correlated to the data at the current time step, these correlations are remembered and otherwise they are forgotten.