RNN

• RNN stands for Recurrent Neural Network. It's a type of artificial neural network designed to recognize patterns in sequences of data and make predictions based on that data. Unlike traditional feedforward neural networks, which process data in a single direction (from input to output), RNNs have connections that form directed cycles, allowing them to exhibit dynamic temporal behavior.

 

• This cyclic structure enables RNNs to maintain a memory of previous inputs, making them particularly effective for tasks involving sequential data, such as time series analysis, natural language processing, speech recognition, and handwriting recognition.
• RNNs are composed of units called neurons, organized into layers. Each neuron receives input from the previous time step's output, along with the current input, and produces an output that serves as input for the next time step. This recursive process allows RNNs to capture temporal dependencies within the data.

 

• Despite their effectiveness, traditional RNNs can suffer from the vanishing gradient problem, where gradients diminish exponentially over time, making long-term dependencies challenging to learn. To address this issue, various extensions of RNNs have been developed, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), which are designed to better capture long-term dependencies and mitigate the vanishing gradient problem.

 

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