Backpropagation algorithm steps

1. What are the steps in the backpropagation algorithm?
2. What is backpropagation and its process?
3. What is backpropagation formula?
4. How is backpropagation implemented?

What are the steps in the backpropagation algorithm?

Backpropagation Algorithm:

Step 1: Inputs X, arrive through the preconnected path. Step 2: The input is modeled using true weights W. Weights are usually chosen randomly. Step 3: Calculate the output of each neuron from the input layer to the hidden layer to the output layer.

What is backpropagation and its process?

Backpropagation, or backward propagation of errors, is an algorithm that is designed to test for errors working back from output nodes to input nodes. It is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning.

What is backpropagation formula?

∂ajk​∂alk+1​​=wjlk+1​g′(ajk​). Plugging this into the above equation yields a final equation for the error term δ j k \delta_j^k δjk​ in the hidden layers, called the backpropagation formula: δ j k = ∑ l = 1 r k + 1 δ l k + 1 w j l k + 1 g ′ ( a j k ) = g ′ ( a j k ) ∑ l = 1 r k + 1 w j l k + 1 δ l k + 1 .

How is backpropagation implemented?

To apply the backpropagation algorithm, our activation function must be differentiable so that we can compute the partial derivative of the error with respect to a given weight w_{i, j}, loss (E), node output o_j, and network output net_j.