Neural networks can vastly improve the decision processes and help to solve complex business problems and real-life situations. In this article, let’s examine the scope of neural networks in detail.
Neural networks are interconnected nodes that mimic the functions of the human brain. Our brain cannot solve complex data and is prone to errors and bias. A neural network overcomes this limitation by analyzing how the human brain works and replicating the functions of neurons in the brain. These networks resemble the biological neural networks present in our brain and process the data inputs using machine learning algorithms.
The nodes in the neural networks are connected and function as a whole by exchanging information. Algorithms used in the process help to recognize the hidden patterns and classify the data and are programmed for continuous learning. The nodes comprise many layers that execute a specific and defined function when it receives data. The data is processed by the multiple layers and finally the output layer provides the result in an understandable form.
Keep reading to learn more about the types of neural networks and its benefits.
Types of neural networks
Neural networks are classified based on the hidden layers and the depth of the network. Here are the types of neural networks that are popular today.
- Feed-forward neural networks – These are basic neural networks where information travels in a single direction from the input node to the output node.
- Recurrent neural networks – These are more complex networks where information travels in multiple directions and improves the functions through learning.
- Convolutional neural networks – These networks can perform face recognition and also encode attributes assuming it as an image.
Advantages of neural networks
Neural networks offer several advantages and are changing the way enterprises solve problems and make decisions. A few of the important advantages of neural networks are mentioned below.
- Self-learning – Neural networks learn by themselves and produce non-linear, complex relationships. The output is never limited to the input and is important since the real-life problems follow a similar pattern.
- Generalization ability – Once the neural networks begin to learn from the initial inputs, it can further interpret and generalize on the unseen data. This makes it an excellent predictive tool for business decisions.
- Multi-tasking potential – Neural networks can perform multiple tasks parallely. The system performance is not affected in any manner making it highly useful for complex business situations.
- Solve real-time events– These networks learn from examples and develop the ability to apply the learning to similar events. Hence, it can be applied effectively in real-time events.
- Advanced forecasting ability – Since neural networks do not impose any kind of restrictions on the input it is useful for predicting in situations where there is high data volatility. Neural networks also have the ability to learn the hidden relationships without imposing any kind of fixed relationships.
- Efficiency – The input in neural networks is not stored in any database. It is stored within its own network so that data loss does not have any impact on the working of the system. If a neuron fails to respond or any information is missing, the network can identify the fault and continue to produce the output.
Neural networks are being widely used to solve complex business problems and the demand is only increasing with time. Many sectors including banking, medicine, security, agriculture, government, etc are using the power of neural networks to improve the performance. The adaptability of neural networks increases with time since it learns from real-time experiences. Neural networks have taken us one step closer to human level artificial intelligence and are expected to grow further in the coming years.
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