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.