> 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/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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/sequence-models/recurrent-neural-networks.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.