Recurrent Neural Networks

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Recurrent Neural Networks (RNNs) are a class of neural networks designed to recognize patterns in sequences of data, such as time series or natural language. Unlike traditional neural networks, RNNs have a unique architecture that allows them to maintain a form of memory, enabling them to use information from previous inputs to influence current outputs. This is particularly useful for tasks where context is important, such as language modeling or predicting future values in a time series.

The architecture of RNNs involves loops in the network, allowing information to be passed from one step of the sequence to the next. This means that the output from one neuron can be fed back into the network as input for the next time step. However, traditional RNNs can struggle with long-term dependencies due to issues like vanishing gradients, which is why Long Short-Term Memory (LSTM) networks, a specialized type of RNN, are often used for more complex tasks ¹⁵.

If you’re interested in learning more about RNNs, their applications, or how to implement them, feel free to ask!

Towards Data Science

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