# What is propagated in the backward pass?

## What is propagated in the backward pass?

In the backward pass, the flow is reversed so that we start by propagating the error to the output layer until reaching the input layer passing through the hidden layer(s). The process of propagating the network error from the output layer to the input layer is called backward propagation, or simple backpropagation.

**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 propagation and backward propagation?**

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 back propagation algorithm in neural network?

The Back propagation algorithm in neural network computes the gradient of the loss function for a single weight by the chain rule. It efficiently computes one layer at a time, unlike a native direct computation. It computes the gradient, but it does not define how the gradient is used.

**What is back propagation 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. Explanation: RNN (Recurrent neural network) topology involves backward links from output to the input and hidden layers.

**How back-propagation is used in classification?**

The backpropagation is a machine learning algorithm using for training the neural network for various problem-solving. The backpropagation is a machine learning algorithm using for training the neural network for various problem-solving.

#### Why is backpropagation more efficient?

In fitting a neural network, backpropagation computes the gradient of the loss function with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually.

**What are back propagation networks explain its architecture and characteristics?**

A back propagation neural network is a multilayer, feed-forward neural network consisting of an input layer, hidden layer and an output layer. The neurons present in the hidden and output layers have biases, which are the connections from the units whose activation is always 1.