Artificial Neural Networks are progressive learning systems modeled after the human brain that continuously improve their function over time. Artificial neural networks can be effective in gathering and extracting the relevant information from big data, identify valuable trends, relationships and connections between the data, and then rely on the past outcomes and behaviors to help identify and implement the best marketing tactics and strategies.
The human brain consists of 100 billion cells called neurons. The neurons are connected together by synapses. If sufficient synaptic inputs to a neuron fire, that neuron will also fire. This process is called “thinking”.
Artificial neural networks are a form of computer program modeled after the way the human brain and nervous system works. It is not necessary to model the biological complexity of the human brain at a molecular level, just its higher level rules.
Common practical uses of Neural Networks are for character recognition, image classification, speech recognition, facial recognition, etc. They are also used for Predictive Analytics. Read our article on HOW LEADING TECHNOLOGY COMPANIES ARE USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING.
Artificial Neural Networks(ANN) are a series of interconnected artificial processing neurons functioning in unison to achieve desirable outcomes.
Artificial neurons are elementary units in an artificial neural network. Each artificial neuron receives one or more inputs and sums them to produce an output. Think of artificial neurons as simple storage containers.
Artificial Neural Networks are comprised of three main parts: the input layer, the hidden layer, and the output layer. Note that you can have n hidden layers. The term “deep” learning implies multiple hidden layers. Each layer is a one dimensional array.
During processing each input is separately weighted, and the sum is passed through a non-linear function known as an activation function or transfer function to the next set of artificial neurons.
Using trial and error learning methods neural networks detect patterns existing within a data set ignoring data that is not significant, while emphasizing the data which is most influential.
From a marketing perspective, neural networks are a form of software tool used to assist in decision making. Neural networks are effective in gathering and extracting information from large data sources and identify the cause and effect within data. These neural nets through the process of learning, identify relationships and connections between databases. Once knowledge has been accumulated, neural networks can be relied on to provide generalizations and can apply past knowledge and learning to a variety of situations.
Neural networks help fulfill the role of marketing companies through effectively aiding in market segmentation and measurement of performance while reducing costs and improving accuracy. Due to their learning ability, flexibility, adaption and knowledge discovery, neural networks offer many advantages over traditional models. Neural networks can be used to assist in pattern classification, forecasting and marketing analysis.