In this modern era of business, having automated business processes in place can save organizations a lot of time and stress with the right algorithms in place. One of the ways this is accomplished is through what’s referred to as an artificial neural network (ANN). More commonly known as neural networks (NN), these computing systems are a machine-inspired delivery of how the human brain functions. As a now key part of data science, NNs are changing how algorithms pace business procedures.

Understanding Neural Networks

You may be asking yourself, what is a neural network? These are networks that learn by example in a manner similar to the human brain. External inputs are received, processed, and actioned in the same way as the brain. The brain is wired differently in different sections, and these parts of the human brain are arranged hierarchically in levels. As information enters the brain, each layer of neurons does its particular job of processing information, deriving insights and passing them to the next layer. Neural network research operates in a fashion similar to how the brain interprets data input for thought, decision-making, memory, reasoning, and action.

Artificial neural networks simulate this multilayered approach to processing various information inputs and basing decisions on that relevant information. Neurons, as the nerve cells of the brain, have extensions known as dendrites that receive signals and then transmit them to the cell body. The cell body processes stimuli and makes decisions to act upon those stimuli. The working of neural networks is inspired by the function of the neurons in the brain, even though the technological mechanism of action is different from the biological one.

Neural Networks and Deep Learning

Neural networks are nothing without deep learning capabilities. These two terms are closely connected, dependent on each other’s functions. Deep learning forms the cutting edge of artificial intelligence. While deep learning and machine learning may find themselves in the same sentences, they differ in that deep learning is designed to teach computers how to process and learn from data sets. With deep learning, the computer continually trains itself, learning from data and forming algorithms that create more capabilities across artificial neural networks.

Complex neural networks contain an input layer and an output layer but also pack in multiple hidden layers. They are called deep neural networks and are conducive to deep learning. This system teaches itself and becomes more “knowledgeable” as it goes along, filtering information through these hidden layers similar to how the human brain does. Deep learning affords organizations in all sectors the analytical tools that can come through data sources effortlessly, reducing the need for human intervention in these monotonous tasks.

Neuronal Workings

Neurons within NNs and ANNs receive a stimulus in the form of a signal that is a real number. From there, the output of each neuron is computed by a nonlinear function of the sum of the inputs of those data sources. These connections among neurons are referred to as edges and, like neurons, carry a certain weight. The parameter adjusts and changes as deep learning proceeds. The weight will increase or decrease based on the strength of the signal at a connection. Neurons may have a threshold, but a signal is sent onward only if the aggregate signal crosses that threshold.

Neurons aggregate into layers. These different layers may perform different modifications on their inputs. This goes from the first layer, known as the input layer, to the last layer, known as the output layer. An artificial neural network takes data through that entry point and processes the information in the hidden layer, only to produce insights and analytics on how to move forward based on the information received. The truth is that a neural network can help decision-makers get a better understanding of the different ways they can benefit their businesses in real time.