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What is backpropagation?

Backpropagation is an algorithm that uses gradient descent for supervised learning of artificial neural networks. Short for backward propagation of errors, it calculates the gradient of the error function with regard to the neural network's weights.

The gradient of the final layer of weights is calculated first in backpropagation, and the gradient of the first layer of weights is calculated last. The calculation of e gradient of error essentially occurs backwards through the artificial neural network.

It reuses partial computations of the gradient of a layer while calculating the gradient of the next layer.

Backpropagation was one of the very first techniques that showed that it was possible for artificial neural networks to learn good internal representations. 

On inspecting multilayer feedforward networks that were trained with the use of backpropagation, it came to light that multiple nodes learned features that were akin to those designed by human experts. 

The algorithm was found to be so efficient that it did not require human experts to discover appropriate features. Because of that, problems that could not be handled by artificial neural networks were now fair game with backpropagation.

Here’s a way for you to get an idea of what backpropagation is like. In the movie Men in Black III, Will Smith’s character has a fight with a villian in which he got shot. But, he had a way of traveling back in time, knowing where the projectile would hit and thus dodging it. This time he got a little further, but got hit by the next projectile, so he went back in time again, and dodged both projectiles. He kept making more progress, getting hit at some point, and going back in time till he reached so close to the villian that he could attack him.

That’s similar to what happens in backpropagation. The backpropagation algorithm goes back from the results obtained and rectifies its errors at every node of the neural network so as to improve the performance of that neural network model. 

Source: Towards Data Science

What is the objective of backpropagation?

Backpropagation algorithms are essentially the most important part of artificial neural networks. Their primary purpose is to develop a learning algorithm for multilayer feedforward neural networks, empowering the networks to be trained to capture the mapping implicitly.

Its goal is to optimize the weights, thus allowing the neural network to learn how to correctly map arbitrary inputs to outputs.

How does backpropagation work?

There are four layers in a backpropagation network. These are the input layer, hidden layer, hidden layer II and final output layer. Every layer works in its own way to take action in order to get the results that we desire and correlate the scenarios to the conditions.

Here’s how backpropagation works:

  • The input layer receives the input x.
  • Weights w are used to model the input.
  • The output is calculated by every hidden layer and data is ready at the output layer
  • We find the error by observing the difference between the actual output and the desired output.
  • Go back and adjust the weights in the hidden layer to minimize the error in future runs.

The training phase is done under supervision and the process mentioned above needs to be repeated until the actual output matches the desired output. Once that is done, the model can be used in production.

What does the loss function do?

One or multiple variables will be mapped to real numbers. These represent a price related to those numbers. The loss function will calculate the difference between the network output and its probable output.

What are the advantages of backpropagation?

Backpropagation has numerous advantages, here are some of the most significant ones.

  • No parameters need to be tuned
  • The model does not need to learn the features of the function
  • Backpropagation is a flexible method because prior knowledge of the network is not required.
  • It is a fast method and is rather easy to implement.
  • The approach tends to work rather well in most situations.
  • The user does not need to learn special functions.

What are the disadvantages of backpropagation?

The biggest disadvantages of backpropagation are:

  • Backpropagation could be rather sensitive to noisy data and irregularity.
  • The performance of backpropagation relies very heavily on the training data.
  • Backpropagation needs a very large amount of time for training.
  • Backpropagation requires a matrix-based method instead of mini-batch.

What are the types of backpropagation networks?


Two kinds of backpropagation networks exist: static and recurrent.

Static backpropagation

In static backpropagation, static outputs are generated by mapping static inputs. Static backpropagation networks have the ability to solve static classification problems like optical character recognition.

Recurrent backpropagation

We conduct recurrent backpropagation until we reach a specific threshold. After that, we calculate the error and propagate it backward. 

In static backpropagation, immediate mapping is possible, while in recurrent backpropagation, immediate mapping does not happen.

Is backpropagation greedy?

Backpropagation is significantly faster than other neural network algorithms. You could consider backpropagation to be like an advanced greedy approach. It helps you get the result that you want in a faster manner and has even reduced training times from months to hours. It could pretty much be considered to be the backbone of the neural network.

What is bias in backpropagation?

Most implementations of backpropagation algorithms include an extra class of weights that are called biases. These biases are essentially values that get added to the sums calculated at every node other than input nodes during the feedforward phase.

The negative of a bias could be called a threshold. To keep things simple, biases tend to be visualized as values associated with each node in the intermediate and output layers of a network. In practice, however, they are treated no differently than other weights, with all biases being weights that are associated with vectors leading from a single node whose location is outside of the main network and whose activation is always 1.

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