{"id":24303,"date":"2021-06-14T17:32:00","date_gmt":"2021-06-15T00:32:00","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=24303"},"modified":"2023-06-25T20:36:02","modified_gmt":"2023-06-26T03:36:02","slug":"recurrent-neural-networks","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/recurrent-neural-networks\/","title":{"rendered":"What are Recurrent Neural Networks?"},"content":{"rendered":"\n

Whether you want to build an automated image captioning tool or a machine-based translation tool, the first question you should ask is — What are recurrent neural networks. Recurrent neural networks (RNNs) give an interesting twist to the simple neural networks by remembering the past data. While simple neural networks just give fixed-sized input and outputs, RNN memorise the previous inputs in order to give sequential data as output.<\/p>\n\n\n\n

Understanding What are Recurrent Neural Networks<\/h2>\n\n\n\n

A recurrent neural network or RNN helps process sequences like sentences, daily stock prices, or even sensor measurements. RNN is a set of algorithms which helps in processing sequences by retaining the memory (or state) of the previous value in the sequence.<\/p>\n\n\n\n

Recurrent mainly means that the output of the current time step actually becomes the input for the very next time step. That means, at every element, the model not only considers the current input but also remembers all the previous elements.
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Image Credit: Pintrest<\/figcaption><\/figure>\n\n\n\n

Recurrent neural networks are designed to mimic the way human brains think while processing and sequencing. For instance, we consider the entire sentence before speaking or writing instead of just considering the words we want to use. Similarly, the memory of RNNs allows it to learn more about long-term dependencies which in turn allows it to take the entire context of the sequence into account before making the next prediction.<\/p>\n\n\n\n

For building RNNs, you can use Keras, the neural network library which is built in Python. Although there are many other neural network libraries that can be used for RNN, Keras is often preferred because of its fast development time, ease of use, and support for Python. With Keras, you can also easily build text generation and predictive models using RNN<\/a>.<\/p>\n\n\n\n

The advantages of using RNN structure include:<\/p>\n\n\n\n