In this article, I will explain What are Recurrent Neural Networks.
Recurrent Neural Networks (RNNs) are a type of neural network commonly used for sequence modeling tasks such as natural language processing and speech recognition. Unlike feedforward neural networks, which process inputs independently and produce outputs based solely on the current input, RNNs have a “memory” that allows them to take into account the sequence of past inputs when making predictions.
The basic idea behind RNNs is to use a hidden state that is updated at each time step based on the current input and the previous hidden state. The hidden state can be thought of as a summary of the previous inputs and is used to make a prediction for the current input. The updated hidden state is then passed on to the next time step, allowing the network to remember information from previous time steps.
One of the most popular variants of RNNs is the Long Short-Term Memory (LSTM) network, which uses a more sophisticated gating mechanism to selectively remember or forget information from the past. Another popular variant is the Gated Recurrent Unit (GRU), which is similar to LSTM but uses fewer parameters and is faster to train.
RNNs are powerful models for sequence modeling because they can capture complex dependencies between inputs in a sequence and use this information to make accurate predictions. However, they can be difficult to train and prone to issues such as vanishing or exploding gradients, which can limit their performance on long sequences.
Further Reading
Deep Learning Practice Exercise
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