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Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa.

## What is backpropagation with example?

Backpropagation is one of the important concepts of a neural network. For a single training example, Backpropagation algorithm calculates the gradient of the error function. … Backpropagation can be written as a function of the neural network.

## How do you explain back propagation?

“Essentially, backpropagation evaluates the expression for the derivative of the cost function as a product of derivatives between each layer from left to right — “backwards” — with the gradient of the weights between each layer being a simple modification of the partial products (the “backwards propagated error).”

## What is forward and backward propagation in neural network?

Forward Propagation is the way to move from the Input layer (left) to the Output layer (right) in the neural network. The process of moving from the right to left i.e backward from the Output to the Input layer is called the Backward Propagation.

## What is the main purpose of the backpropagation?

The goal of backpropagation is to compute the partial derivatives ∂C/∂w and ∂C/∂b of the cost function C with respect to any weight w or bias b in the network.

## What is back propagation Geeksforgeeks?

Back-propagation, short for “backward propagation of errors,” is an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the method calculates the gradient of the error function with respect to the neural network’s weights.

## What is back propagation in neural network Mcq?

What is back propagation? Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

## When was back-propagation invented?

Efficient backpropagation (BP) is central to the ongoing Neural Network (NN) ReNNaissance and “Deep Learning.” Who invented it? Its modern version (also called the reverse mode of automatic differentiation) was first published in 1970 by Finnish master student Seppo Linnainmaa.

## Why we use forward and backward propagation?

In the forward propagate stage, the data flows through the network to get the outputs. The loss function is used to calculate the total error. Then, we use backward propagation algorithm to calculate the gradient of the loss function with respect to each weight and bias.