{"id":6943,"date":"2025-03-05T12:39:00","date_gmt":"2025-03-05T11:39:00","guid":{"rendered":"https:\/\/www.artea.com\/what-are-recurrent-neural-networks\/"},"modified":"2026-09-29T09:29:42","modified_gmt":"2026-09-29T07:29:42","slug":"what-are-recurrent-neural-networks","status":"publish","type":"post","link":"https:\/\/www.artea.com\/en\/what-are-recurrent-neural-networks\/","title":{"rendered":"WHAT ARE RECURRENT NEURAL NETWORKS?"},"content":{"rendered":"\t\t
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Recurrent neural networks (RNNs)<\/strong> are a type of neural network<\/a> designed to process sequential data, such as text, audio signals, and time series<\/strong>.<\/p>

The defining feature of RNNs is their ability to store past information through a recurrent mechanism<\/strong>, which allows them to retain a sort of “memory” of previous data. This capability makes them particularly well-suited for tasks in which context is essential for making accurate predictions. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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How Recurrent Neural Networks Work<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Recurrent neural networks are characterized by connections that create loops within the network<\/strong>, allowing information to be retained and reused during the processing of a sequence. Unlike feedforward neural networks, which process input in a single direction, RNNs can use past information to improve future predictions. <\/p>

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The basic structure of RNNs involves each neuron taking both the current data and the previous state as input, thereby updating the network’s internal state. This process continues throughout the entire sequence, creating an internal representation that accounts for the entire past context. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Vanishing Gradient Problem<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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One of the main challenges of RNNs is the “vanishing gradient” problem<\/strong>. This problem occurs during the training process, when the gradients used to update the network\u2019s weights become extremely small. This makes it difficult for the network to learn long-term relationships in sequential data, since the weight updates become negligible. <\/p>

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As a result, RNNs tend to \u201cforget\u201d information further back in the sequence, limiting their effectiveness on long sequences. To address this problem, variants such as Long Short-Term Memory (LSTM)<\/strong> and Gated Recurrent Units (GRU)<\/strong> have been developed, which introduce mechanisms to retain information for longer periods of time. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Long Short-Term Memory (LSTM)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Long Short-Term Memory (LSTM)<\/strong> networks are a variant of RNNs designed to overcome the vanishing gradient problem and improve the network’s ability to handle long-term dependencies. LSTMs use special memory cells and three types of gates (input, output, and forget) that regulate the flow of information within the network. This allows LSTMs to selectively retain or forget information, improving their ability to learn complex relationships across long sequences.

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