Feedback Layer relevancy of Neural Network

1. What is the Feedback Layer ?

Imagine we’re learning how to shoot a basketball.

  • We throw the ball.
  • If it goes in, we feel good.
  • If it misses, we adjust how we throw the next time.

That “adjustment” is based on feedback — and that’s exactly what happens in a Feedback Layer of a neural network.

In the context of a neural network:

  • The feedback layer is not a physical layer like input or hidden layers.
  • It’s part of the learning loop.
  • After the network makes a prediction, it compares that prediction to the correct answer.
  • Then, it calculates how wrong it was (this is called loss or error).
  • This error is sent backward through the network to adjust the weights — that process is called backpropagation.
  • So, the feedback layer helps the network learn from its mistakes.

Analogy:

  • Neural network = Student
  • Output = Answer on the test
  • Feedback layer = Teacher correcting the answer
  • Backpropagation = Student learning from the mistake and improving next time

So, what does the feedback layer do?

Step What Happens
1 Neural net gives an answer (output)
2 We check how far off that answer is (calculate error)
3 We send that error back (feedback) to update the thinking process (weights)
4 Neural net improves for next time

Feedback Layer relevancy of Neural Network – Feedback Layer example with Simple Python