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 main unit of the brain is neurons, the basic building block of an artificial neural network is a sensor that performs simple signal processing, which is then connected to a large network network.
The computer with the neural network is taught to perform a task by having pre-labeled training samples analyzed. A common example for a neural network that uses deep learning is the recognition of an object in which the neural network is presented with a large number of objects of a certain type, such as a cat or computer. It learns to categorize new images by analyzing repetiating patterns in presented images.
How does neural networks learn?
Unlike other algorithms, neural networks cannot be programmed directly with deep learning. On the contrary, just like in a child's developing brain, they need to learn information. Learning strategies are applied in three ways:
- Supervised learning: This learning strategy is the simplest because it is a dataset that the computer passes through and the algorithm is changed until you process 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 away the target is. The neural network is then adjusted to improve the accuracy of the algorithm.
- Empowered learning: In this algorithm, the neural network is strengthened for positive results and is unlikely to produce negative results.
Advantages of artificial neural networks
- With parallel processing capability, it can do multiple jobs at the same time.
- Information is stored throughout the network, not just in a database.
- It can extract missing information depending on how important the missing information is.
- It can model and data large data with the ability to learn about hidden relationships in data without commanding any fixed relationships.
- It can predict the output of the data with the ability to generalize and infer relationships on invisible data.
Disadvantages of artificial neural networks
- Artificial neural network architecture can only be found through trial and error and experience to determine the appropriate network structure.
- It needs a high processor requirement for parallel processing capabilities.
Areas of use 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 other areas, including:
· Stock market forecast
· Delivery driver route planning and optimization
· Drug discovery and development
These are several specific areas in which neural networks are applied today.