Backward propagation of errors or backpropagation has been an important artificial intelligence
algorithm used in the supervised and unsupervised learning of artificial neural networks
since it was popularized in the 1986 paper of Geoffrey Hinton
, David Rumelhart, and Ronald J. It has been instrumental in advancing the applications of neural networks
and the expansion of deep learning
. Some of its more specific applications are within the realms of speech recognition, image recognition, and natural language processing
A Simplified Explainer of What Backpropagation Is and How It Works
General Overview and Description
It is important to note that backpropagation is an AI algorithm
designed to test for errors working back from output nodes to input nodes. Hilton and his colleagues were not the first to use this approach in training artificial neural networks but their highly cited paper has produced related studies detailing more efficient and effective methods that enabled faster and more accurate training of artificial neural networks
The algorithm operates by calculating the error or loss between the predicted output and the true output of the neural network. It then uses the identified and calculated error to update the weights of the network to minimize the error in future predictions. What this means is that it produces and uses a combination of numbers, called weights, to come up with different solutions until it gets the correct given output. Backpropagation algorithm essentially helps a neural network to learn by giving it feedback on its performance.
Remember that it enables the artificial neural network to adjust its weights and biases so that it can make more accurate predictions or classifications based on input data. The importance of this algorithm lies in its ability to improve the accuracy of the predictions of the neural network over time. Furthermore, because it can learn from a large amount of data, it has been a critical component of modern machine learning
Analogical Explanation of Operation
Using an analogy can help better understand how backpropagation works and why it has become an important algorithm in artificial intelligence. Nevertheless, imagine this algorithm as a group of friends playing a game where they have to guess the right answer to a question. Each member has to guess while also helping one another to come closer to the right answer. The entire group keeps track of how close each guess is to the right answer.
The group also evaluates each guess that is closest to the right answer and then encourages the involved members to think about how they can adjust their guess to draw them even closer the next time. The entire process looks as if the group has all members providing their respective guesses while also helping one another adjust their guesses and provided additional guesses that are closers to the right answer. The whole group learns through the combination of individual efforts, trial-and-error procedures, and collaboration.
Nevertheless, based on the illustration, think of the group as an artificial neural network. The members represent artificial neurons. Each neuron is responsible for guessing the answer. The whole network tracks all guesses closer to the right answer and goes back and adjusts the guesses of each neuron so that it can get even closer to the right answer next time. This process of going back and adjusting the guesses is at the heart of backpropagation.