What is backpropagation? How does it work? Why do we need it?

The Backpropagation algorithm looks for the minimum value of the error function in weight space using a technique called the delta rule or gradient descent. The weights that minimize the error function is then considered to be a solution to the learning problem.

We need backpropogation to,

  • Calculate the error – How far is your model output from the actual output.
  • Minimum Error – Check whether the error is minimized or not.
  • Update the parameters – If the error is huge then, update the parameters (weights and biases). After that again check the error.
    Repeat the process until the error becomes minimum.
  • Model is ready to make a prediction – Once the error becomes minimum, you can feed some inputs to your model and it will produce the output.

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