What is a neural network?
As computers become more and more powerful, have you ever wondered if the day would come that these could imitate our brain’s operations? Well, that day may not be too distant anymore. In fact, it’s already here because of neural networks.
With neural networks, computers can now copy some of the more straightforward operations of the neurons of the human brain. Through software simulations, even your home computer can learn things, understand patterns, and make decisions as you would. At least, to a limited extent.
Our brains are sophisticated machines, and up to this point, even the most mighty IBM mainframes are still unable to imitate its functions adequately. That’s why when we talk about neural networks, we are talking about Artificial Neural Networks (ANNs) and not the Real Neural Networks (RNNs) which refers to the living neurons of the human brain.
What are the parts of a neural network?
A neural network can be any number of artificial neurons called units. These units are interconnected and arranged in layers opposite each other depending on whether they are input units, hidden units, or output units.
- Input units—As the name suggests, input units are designed to accept information from the outside world. The source of information can be a human operator or another computer. The network then processes, recognizes and learns the news.
- Hidden units—This unit functions as the artificial brain of the network. Its job is to transform the input into something that the output unit can process. Suppose you want the computer to identify a bus in a picture. A bus detector in the hidden unit can be composed of a wheel detector, a box detector since buses do look like a huge box, and a size detector to differentiate a car from a bus. These are the tools within the hidden unit designed to identify buses.
- Output units—The job of the output unit is to send signals, or outputs, of the information it has learned. In our analogy, the output unit might signal that it has identified a bus in the picture that the hidden unit has processed.
- Weight—This refers to the interconnection between the units. It can either be positive if a group excites another or contrary if one unit inhibits another. A unit has higher influence over another if it has a higher weight. It is how brain cells influence each other across the synapse.
How do neural networks learn?
There are two ways in which information flows through the neural network. First is through the “feedforward” direction which happens when the system is learning. Here, the input unit is fed with patterns of data. When a particular threshold is reached, it excites the hidden group which in turn triggers the output unit when it reaches its limit.
The second direction is “backpropagation.” It involves comparing the output that the network is expected to produce from what it delivers. The difference is used to modify the weight of the connections from the output unit, to the hidden group all the way back to the input unit. Over time, this process causes the network to learn by reducing the difference between the actual and the intended output until both coincide.
Humans learn in similar ways. If you want your child to learn how to bat, you show him the steps involved (feedforward). Then you also teach him how to execute the steps in a single action (intended output). When the child imitates (actual production), you provide feedback on what he has done right or wrong (the difference between real and intended output). You repeat the process until the child learns to bat. The difference between your execution and his becomes negligible over time (backpropagation).
Applications of neural network
Neural networks are used extensively in business forecasting, fraud detection, risk assessment and reduction, and marketing research. It’s also used to analyze price data to make trading decisions. Using a well-researched input on a targeted indicator, an investor can expect 10% increased efficiency in a technical analysis using a neural network.
The tech giant Google is already using neural networks so that its self-driving car can detect pedestrians and avoid running over them. As technology improves, we will see neural networks slowly getting more involved in our lives.