People started working on artificial intelligence back in the late ’60s. After they came up with the concept of perceptron, this field looked very promising. But as the years passed by, no significant development took place even after making several attempts from multiple directions! As people were beginning to lose hope, backpropagation came into picture and breathed new life into this field. Backpropagation was the result of pioneering work by mathematicians and computer scientists, which eventually led to a successful revival of artificial intelligence! So what exactly is backpropagation? How is it used in real life?

**What does it mean?**

Multi-layer networks often use a variety of learning techniques for training, the most popular being backpropagation. Here, the output values are compared with the correct answer to compute the value of some predefined error-function. By various techniques, the error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by some small amount. After repeating this process for a sufficiently large number of training cycles, the network will usually converge to some state where the error of the calculations is small.

**Why do we need it?**

An artificial neural network consists of perceptrons. Perceptrons, by themselves, are a bit limited. They are very simplistic in their design and minimal in their functionality, but that is not entirely a bad thing. In fact, perceptrons are appealing *because* of their simplicity! Perceptrons enable a pattern to be broken up into simpler parts that can each be modeled by a separate perceptron in a network. So, even though perceptrons are limited, they can be combined into one powerful network that can model a wide variety of patterns.

We can design many complex boolean expressions of more than one variable. Boolean expressions are the basic building blocks of many different circuits and networks. These algorithms, however, are more complex in arrangement, and thus the learning function is slightly more complicated. For many problems (specifically, the linearly separable ones), a single perceptron will do. The learning function for it is quite simple and easy to implement. The perceptron is an elegantly simple way to model a human neuron’s behavior. Once we have the perceptron set up, we use backpropagation to reiterate and fine-tune our neural network to obtain a robust model. We can think of backpropagation as a feedback-loop equivalent for a machine learning algorithm. This is an extremely important concept in robotics and artificial intelligence. Many algorithms are built around this to put social and emotional intelligence into a robot.

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