Understanding Backpropagation in Neural Networks: An Example-Based Guide

Backpropagation might sound a bit gimmicky, but in simple terms, it is the route through which advanced neural network models train themselves. It helps a neural network adjust its internal workings for better time-based predictions. In this article, we will simplify the concept of backpropagation with an example. 

What is Backpropagation?

Backpropagation is a computer algorithm used to train neural networks. A neural network’s predictions might be wrong, so data scientists use backpropagation to remove errors in these predictions. It removes the existing errors in the model and helps it improve. 

The functioning of backpropagation is based on the concept of deviation or loss, that is, how far off from reality the projections made by the model are. It traces all the hidden layers in the model to identify each layer’s contribution to the loss. 

After evaluating the individual contributions of the hidden layers to the loss, minor adjustments are made to the connection weights. These corrections ensure that the loss decreases over time and the predictions improve. A backpropagation model propagates the error signals backwards as input. This process is repeated until the network model minimises prediction error and loss function.   

Understanding Backpropagation With Easy Examples

Let us understand the workings of backpropagation in neural network models with the help of the following examples. 

Example 1:

Let us say that our neural network model takes a handwritten digit as input and predicts what the digit is. What matters the most in data-driven neural network models is not just the prediction itself but the confidence interval of the prediction. 

For example, a neural network model displaying a result of “digit 5” with 25% confidence and “digit 3” with a 75% confidence interval for the input of “digit 5” is certainly not the result for which you would be ready to burn your midnight oil. 

It is here that backpropagation would come into the picture. Taking stock of the prediction errors (0.75 for digit 3 and 0.25 for digit 5), the model would adjust the connection weights and help the model improve.

Readjustments are made in the hidden layer that contributed most significantly to the erroneous calculations. Such hidden layers that make erroneous predictions (contributions) are also known as weak connections. Thanks to backpropagation, the problem of weak connections is solvable. Backpropagation minimises erroneous connections by altering the weights assigned to such connections. 

With enough training data, the model can pass this error correction cycle (the backpropagation chain rule) and gradually move towards correctly identifying numerical digits. 

Example 2: 

Let us now take an example of a neural network developed to identify whether a particular image contains a cat or a dog. As an image processing model, it takes pixels as input. It passes them through the input layer and several hidden layers to perform feature extraction and present an output of the probability of the image being of a cat (or a dog). 

Suppose you feed the model the image of a dog, and it displays the result as a cat with 90% probability (confidence). The whole purpose of developing such a model can only be saved with backpropagation. You serve the hidden layers in the model with more data until it achieves the desired level of output accuracy. 

Backpropagation’sBackpropagation’s functioning is based on advanced mathematical concepts. It uses differential calculus to calculate the loss function’s variation rate. This rate is beneficial in estimating deviations from the desired results. Adjustments are made in the hidden layers of the model based on erroneous signals, which are input through backpropagation. 

Through iterations, backpropagation ensures minimum prediction error by tweaking individual hidden layers, influencing inaccuracy. It is the go-to technique for improving the accuracy of neural network models. 

Conclusion

Backpropagation is an advanced method for training neural network models themselves. A neural network can make better predictions based on backpropagation. In this article, we’ve demystified the concept of backpropagation with suitable examples.