Understanding RNNs: Why Not compare it with FNN to Understand the Math Behind it Better? – DAY 58

In this article we try to show an example of FNN and for RNN TO understand the math behind it better by comparing to each other:   Neural Networks Example Example Setup Input for FNN: Target Output for FNN: RNNs are tailored for sequential data because they are designed to remember and utilize information from previous inputs in a sequence, allowing them to capture temporal relationships and context effectively. This characteristic differentiates RNNs from other neural network types that are not inherently sequence-aware., Input for RNN (Sequence): Target Output for RNN (Sequence): Learning Rate: 1. Feedforward Neural Network (FNN) Structure Input Layer: 1 neuron Hidden Layer: 1 neuron Output Layer: 1 neuron Weights and Biases Initial Weights: (Input to Hidden weight) (Hidden to Output weight) Biases: (Hidden layer bias) (Output layer bias) Step-by-Step Calculation for FNN Step 1: Forward Pass Hidden Layer Output: Output: Step 2: Loss Calculation Using Mean Squared Error (MSE): Step 3: Backward Pass Gradient of Loss with respect to Output: Gradient of Output with respect to Hidden Layer: Gradient of Hidden Layer Output with respect to Weights: Assuming : Step 4: Weight Update Update Output Weight: Update Input Weight: 2. Recurrent Neural Network (RNN) Structure Input…