A connection in a synthetic neural network is referred to as an artificial neuron. Similar to the biological brain network of the human body, artificial neural networks have a layered architecture and each network node (connection point) is capable of processing input and transmitting output to other nodes in the network. The nodes are known as neurons in both artificial and biological designs, and the connections are distinguished by synaptic weights, which stand for the importance of the connection. The synaptic weights vary when new data is received and processed, and this is how learning happens.

Artificial neurons are modeled after the way that biological sense systems' neurons are organized in hierarchies. For instance, in the visual system, light input passes via neurons in successive layers of the retina before moving on to neurons in the brain's thalamus and visual cortex. The brain gradually gathers more information as the neurons carry signals through additional layers until it is satisfied it can recognize what the person is viewing. Deep learning is the term used to describe this process of fine-tuning artificial intelligence.

When neurons process the input they receive, they determine whether the output should be passed on to the next layer as input in both artificial and biological networks. Bias refers to the choice of whether or not to transmit information, and it is made by the system's activation function. For instance, an artificial neuron may only transmit an output signal to the following layer if the sum of its inputs, which are voltages, exceeds a predetermined threshold value. Neurons frequently exhibit a wide range of convergence and divergence because activation functions can either be linear or non-linear. One neuron's capacity for convergence is the ability to receive input from many other neurons in the network, whereas convergence is the capacity for one neuron to communicate with many other neurons in the network.

Artificial Neural Networks Advantages and Disadvantages

The simplest definition of the artificial neural network is a model of the human brain made up of neurons. The human brain contains roughly 100 billion neurons. Between 1,000 to 100,000 connections exist between each neuron. Information is distributedly stored in the human brain, allowing us to simultaneously access multiple pieces of information from memory as needed. The statement that a human brain is composed of thousands of extremely potent parallel processors is accurate. Neurons are arranged in multi-layer artificial neural networks in a way that is similar to how they are in the human brain. Each neuron has a certain coefficient for connecting to other neurons. During training, information is distributed to these connection points so that the network is learned.

Advantages of Artificial Neural Networks ( ANN)

Storing information on the entire network: Information such as in traditional programming is stored on the entire network, not on a database. The disappearance of a few pieces of information in one place does not prevent the network from functioning.

Ability to work with incomplete knowledge: After ANN training, the data may produce output even with incomplete information. The loss of performance here depends on the importance of the missing information.

Having fault tolerance: The corruption of one or more cells of ANN does not prevent it from generating output. This feature makes the networks fault tolerant.

Having a distributed memory: For ANN to be able to learn, it is necessary to determine the examples and to teach the network according to the desired output by showing these examples to the network. The network's success is directly proportional to the selected instances, and if the event can not be shown to the network in all its aspects, the network can produce false output

Gradual corruption: A network slows over time and undergoes relative degradation. The network problem does not immediately corrode.

Ability to make machine learning: Artificial neural networks learn events and make decisions by commenting on similar events.

Parallel processing capability: Artificial neural networks have numerical strength that can perform more than one job at the same time.

Disadvantages of Artificial Neural Networks (ANN)

Hardware dependence: Artificial neural networks require processors with parallel processing power, by their structure. For this reason, the realization of the equipment is dependent.

Unexplained behavior of the network: This is the most important problem of ANN. When ANN produces a probing solution, it does not give a clue as to why and how. This reduces trust in the network.

Determination of proper network structure: There is no specific rule for determining the structure of artificial neural networks. The appropriate network structure is achieved through experience and trial and error.

Difficulty of showing the problem to the network:  ANNs can work with numerical information. Problems have to be translated into numerical values before being introduced to ANN. The display mechanism to be determined here will directly influence the performance of the network. This depends on the user's ability.

The duration of the network is unknown: The network is reduced to a certain value of the error on the sample means that the training has been completed. This value does not give us optimum results.

Science artificial neural networks that have stepped into the world in the mid-20th century are rapidly developing. In our present day, we have examined the advantages of artificial neural networks and the problems encountered in the course of their use. It should not be forgotten that the disadvantages of ANN networks, which are a developing science branch, are eliminated one by one and their advantages are increasing day by day. This means that artificial neural networks will become an indispensable part of our lives increasingly important.