# What is back propagation algorithm explain with example?

## What is back propagation algorithm explain with example?

Essentially, backpropagation is an algorithm used to calculate derivatives quickly. Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights. The algorithm gets its name because the weights are updated backwards, from output towards input.

## What is back propagation algorithm explain how is it used for error correction?

What is Backpropagation? 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.

**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 back propagation neural networks explain in detail?**

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 back propagation Mcq?

Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. 3.

### What is back propagation in AI?

Backpropagation is an algorithm used in artificial intelligence (AI) to fine-tune mathematical weight functions and improve the accuracy of an artificial neural network’s outputs. The calculations are then used to give artificial network nodes with high error rates less weight than nodes with lower error rates.

**Which of the following types of machine learning is an example of unsupervised machine learning?**

Some popular examples of unsupervised learning algorithms are: k-means for clustering problems. Apriori algorithm for association rule learning problems.

**What is backpropagation algorithm 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 supervised learning algorithm?

A supervised learning algorithm takes a known set of input data (the learning set) and known responses to the data (the output), and forms a model to generate reasonable predictions for the response to the new input data. Use supervised learning if you have existing data for the output you are trying to predict.

## What is backpropagation algorithm objective?

Explanation: The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.

**What are the 2 types of learning Mcq?**

learning without computers.

**What is the objective of back propagation algorithm?**

### What is back propagation algorithm in machine learning?

This is where the back propagation algorithm is used to go back and update the weights, so that the actual values and predicted values are close enough. In an artificial neural network, the values of weights and biases are randomly initialized.

### What is back propagation in neural networks?

Back propagation in Neural Networks: The principle behind back propagation algorithm is to reduce the error values in randomly allocated weights and biases such that it produces the correct output.

**What is the difference between Forward propagation and back propagation?**

Once the forward propagation is done and the neural network gives out a result, how do you know if the result predicted is accurate enough. This is where the back propagation algorithm is used to go back and update the weights, so that the actual values and predicted values are close enough.

**What is the difference between back propagation and bias training?**

With training, the weights of the bias nodes will also get adjusted to emulate the behavior of the y-intercept c. Back propagation algorithm is a supervised learning algorithm which uses gradient descent to train multi-layer feed forward neural networks.