An artificial neural network is a type of machine learning that models itself after the human brain, creating an artificial neural network that allows the computer to learn by adding new data through an algorithm.
Although the brain's main unit is neurons, the basic building block of an artificial neural network is a sensor that performs simple signal processing, which then connects to a large network network.
A computer with a neural network is taught to perform a task by analysing pre-tagged training samples. A common example for a neural network that uses deep learning is the recognition of an object where the neural network is presented with a large number of objects of a particular type, such as a cat or computer. By analyzing repeated patterns in the images presented, it learns to categorize new images.
How does neural networks learn?
Unlike other algorithms, neural networks cannot be programmed directly with deep learning. On the contrary, just as in a child's developing brain, they need to learn information. Learning strategies are implemented in three ways:
- Supervised learning: This learning strategy is the simplest one, because the computer is a dataset through which it passes and the algorithm is modified until it processes the dataset to achieve the desired result.
- Unsupervised learning: This strategy is used in cases where there is no dataset to learn. The neural network analyzes the dataset and tells the neural network how far the target is. The neural network is then set to increase the accuracy of the algorithm.
- Strengthened learning: In this algorithm, the neural network is strengthened for positive results and is less likely to produce negative results.
Advantages of artificial neural networks
- It can do more than one job at the same time with parallel processing capability.
- Information is stored on the entire network, not just in one database.
- Depending on how important the missing information is, it may remove the missing information.
- It can model big data and large data with the ability to learn hidden relationships in data without commanding any fixed relationships.
- It can predict the output of data with the ability to generalize and infer relations over invisible data.
Disadvantages of artificial neural networks
- The artificial neural network architecture can only be found through trial and error and experience to determine the appropriate network structure.
- It needs high processor requirements for parallel processing capabilities.
Uses of artificial neural networks
Image recognition was one of the first areas where neural networks were successfully implemented, but their use of technology expanded to many more areas, including:
· Stock market forecast
· Delivery driver route planning and optimization
· Drug discovery and development
These are several specific areas where nerve networks are applied today.