recurrent neural networks figure - rnn types based on input and output length when we have an rnn that does not leverage information from the previous statewe have traditional neural net with recurrencehoweverwe have several new possibilities today' most common use cases in nlp revolve around many-to-one and many-to-many models examples include named-entity recognition and machine translation ( translating document from french to englishthis explores few simple examplesbut discussing each variant in depth is beyond the scope of this book readers are strongly recommended to explore named-entity recognitionmachine translation (andoptionallymusic generationindependently let' start with the basics to distinguish between what the rnn produces ( predictionsand what it is ideally expected to produce ( actuals)we denote the predictions by yyyor the output the rnn produces similarlywe will denote the ground truth actual values that rnn should ideally producedenoted by ( ) ( ) (tfigure - shows the outputs (predictionsgenerated by the rnn as ( ( to compute disagreement with actualswe would compare these outputs generated with the actual values represented as ( ) ( ) (