Training a RNN is not a simple task, and it can be extremely computationally demanding sometimes. With long sequences of training data involving many time steps, the training, sometimes becomes extremely difficult. As of now, you have got a better theoretical understanding of how and why backpropagation through time is primarily used for training a RNN. In this section, we will consider a practical example of the use of a RNN and its implementation using Deeplearning4j.
We now take an example to give an idea of how to do the sentiment analysis of a movie review dataset using RNN. The main problem statement of this network is to take some raw text of a movie review as input, and classify that movie review as either positive or negative based on the contents present. Each word of the raw review text is converted to vectors using the Word2Vec model, and then fed into a RNN. The example uses a large-scale dataset of raw movie reviews taken from http://ai.stanford.edu...
