Big data and artificial intelligence are essential tools in today’s business world. Companies of all sizes collect large amounts of data and put them through various analytics processes to get actionable insights. Many of these processes are labor-intensive, requiring the right talent and IT infrastructures to support these heavy-duty processes. Data is the most important resource in the business world, and the companies that do the best job of maximizing their customer and business data have a decisive competitive advantage and are bound to come out on top of their industries. One might argue that big data has gotten so big that it’s taken over the business world.
Just when you thought big data couldn’t get any bigger, it went and did just that. You’re probably familiar with machine learning and the internet of things (IoT), but there’s a technology that builds on those advancements, taking them to new heights, and it’s called the artificial neural network (ANN). ANNs support technology such as deep learning and so much more. It’s an emerging discipline in data science, and it shows a lot of promise, having many use cases in data science and the business world. Continue reading to learn about artificial neural networks and how they function.
What is an artificial neural network?
If you’re not a data scientist, the chances are you have no idea what an artificial neural network is, but it’s something you need to know for your business to survive in the age of Industry 4.0. An artificial neural network is a network of computers that functions like a human brain, collecting and processing information and making decisions based on that information. Indeed, ANNs take machine learning and artificial intelligence a step closer to being human-like.
How do neural networks work?
So, how does an artificial neural network work? Artificial neural networks are layered, having three main layers, which are the input layer, hidden layer, and output layer. Furthermore, there are multiple layers in the hidden layer, and each layer constitutes another step in the learning process. In the hidden layer, the neural network converts the data into information the output layer can use to initiate the desired output.
What is deep learning?
Deep-learning technology relies heavily on ANNs. Deep learning is a field within machine learning based on neural networks that give machines the ability to recognize real-world conditions and make decisions based on those conditions. Some use cases of deep learning technology include speech recognition, image recognition, computer vision, and biosecurity. The most exciting thing is that data scientists are just getting started finding real-world use cases for deep learning.
What are some benefits of artificial neural networks?
As you can imagine, there are plenty of benefits for artificial neural networks. It doesn’t take long to implement learning rules, and then neural networks quickly become sufficient enough to learn on their own. Indeed, they’re capable of supervised learning and unsupervised learning. Furthermore, their numerical strength makes them capable of multitasking. Furthermore, every time data passes through different layers of the ANN, it gets smarter. Indeed, artificial intelligence can get more intelligent through practice.
As big data and artificial intelligence continue to become more vital to the business world, you can expect artificial neural networks to continue to grow more popular as well. ANNs are networks of computers that work together to process information and make reasonable decisions like the human brain. These systems work by collecting data through an input layer and then sending it through hidden layers for pattern recognition and other processing operations so the output layer can use it. Deep neural networks enable data scientists to take big data much further than traditional advanced analytics methods, making it a technology to watch now and in the future.