> For the complete documentation index, see [llms.txt](https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/main-10/sequence-models/recurrent-neural-networks.md).

# Recurrent Neural Networks

RNNs are used when we have sequential data. Some examples of problems that use sequence data include:

* Speech Recognition
* Music Generation
* Sentiment Classification
* DNA Sequence Analysis
* Machine Translation
* Video Activity Recognition
* Name Entity Recognition etc

An RNN learns how to map an input sequence to an output sequence.

The input sequence is denoted by $$X^{\lt 1\gt(i)}, X^{\lt 2\gt(i)}, ..., X^{\lt T\_x^{(i)}\gt(i)}$$where i denotes training examples and $$T\_x^{(i)}$$ is the length of the input sequence of the $$i^{th}$$ training example.

Similarly, the output sequence is denoted by $$Y^{\lt 1\gt(i)}, Y^{\lt 2\gt(i)}, ..., Y^{\lt T\_y^{(i)}\gt(i)}$$where i denotes training examples and $$T\_y^{(i)}$$ is the length of the output sequence of the $$i^{th}$$ training example.

The input and output sequences can have different lengths. This is one of the main reasons why we can't use a standard neural network to learn the mapping from the input to the output sequences. Another reason is because standard neural networks don't share features learned across different positions of a sequence.