Neural Network Training with Recurrent Neural Network (RNN)

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Recurrent Neural Networks (RNNs) are a class of neural networks designed for sequential data processing. Unlike feedforward neural networks, RNNs have connections that form directed cycles, allowing them to maintain a memory of previous inputs. This makes them suitable for tasks where the input data has a temporal or sequential structure. Here are key concepts related to Recurrent Neural Networks (RNNs):

1. Sequence Modeling:

• RNNs are designed to model sequences of data, such as time series, natural language, or any other ordered set of data points.

2. Recurrent Neural Network (RNN) Architecture:

2.1. Hidden State:

• RNNs maintain a hidden state, also known as the memory or internal state. This hidden state serves as a representation of the information the network has seen up to the current time step.
• It allows the network to capture information from previous time steps and retain memory, enabling the model to understand and remember sequential patterns.

2.2. Recurrent Connection:

• The hidden state is updated at each time step based on both the current input and the hidden state from the previous…