The term “neural network” has become a hot topic recently and it is gaining attention from a broad range of people. But, what exactly is a neural network, how do they work and what are the applications?
Over the years, scientists have been working to make computers function more like human brains. Neural networks help scientists achieve this goal since they are basically computer programs assembled from hundreds, thousands and even millions of artificial brain cells that learn and behave very similarly to human brains. You don’t need to program a neural network because it learns all by itself.
Artificial Neural Networks Defined
Artificial neural networks are one of the main tools used in machine learning. They are brain-inspired systems that are intended to replicate the way that humans learn. Neural networks consist of input and output layers, as well a hidden layer of units that transform the input into something that the output layer can use. The big benefit is that neural networks can find patterns that are extremely complex or numerous for a human programmer to extract and teach the machine to learn.
There are several types of neural networks, but the two most common are:
- Feedforward neural network – A basic neutral network in which information travels in only one direction from input to output
- Recurrent neural network – Data flows in multiple directions, possesses greater learning abilities and is used for complex tasks like learning handwriting or language recognition
There are also other networks, including convolutional neural networks, Boltzman machine networks and Hopfield networks. Before choosing a network, you need to consider the data available that can be used to train the network and the specific application use.
How Neural Networks Work
In between the input units and output units is one or more layers of hidden units, which, together, form the majority of the artificial brain. Most neural networks are fully connected, meaning each neuron is connected to every neuron in the previous layer.
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Neural Network Applications
There are many, many applications for neural networks and today, they focus primarily on solving complex signal processing or pattern recognition. Some examples include:
- Handwriting recognition for check processing
- Speech-to-text transcription
- Oil-exploration data analysis
- Weather prediction
- Facial recognition
- Stock market prediction
- Delivery driver route planning and optimization
- Drug discovery and development
- Autonomous cars
Neural networks are behind many of our current technology advances. They are designed for spotting patterns in data and can include classification, clustering and prediction. Neural networks are just one of the exciting new developments that are underway to provide new products and services today and in the future. If you would like additional information, contact your Arrow representative or, if you’re not an Arrow partner, become one today!