Explain Backpropogation in detail?

Data Science, AI and ML
#1

Backpropagation is an algorithm that back propagates the errors from output nodes to the input nodes. Therefore, it is simply referred to as backward propagation of errors. It uses in the vast applications of neural networks in data mining like Character recognition, Signature verification, etc.

The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight via the chain rule, computing the gradient layer by layer, and iterating backward from the last layer to avoid redundant computation of intermediate terms in the chain rule.

Working of Backpropagation:

Neural networks use supervised learning to generate output vectors from input vectors that the network operates on. It Compares generated output to the desired output and generates an error report if the result does not match the generated output vector. Then it adjusts the weights according to the bug report to get your desired output.

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.

Step 4: Calculate the error in the outputs

Backpropagation Error= Actual Output – Desired Output

Step 5: From the output layer, go back to the hidden layer to adjust the weights to reduce the error.

Step 6: Repeat the process until the desired output is achieved.

Advantages:

  • It is simple, fast, and easy to program.
  • Only numbers of the input are tuned, not any other parameter.
  • It is Flexible and efficient.
  • No need for users to learn any special functions.