In computer science, a neural network is a model inspired by the functionality and structure of the human brain. Neural networks form the basis of artificial intelligence (AI) and machine learning (ML). Problems can be solved computer-based using neural networks. The mathematical network models are made up of neurons that are arranged in different layers.
In computer science, neuroinformatics and robotics, neural networks are also known as artificial neural networks, abbreviated to ANN. They are abstract mathematical models whose structure and functionality are modeled on the structure of biological brains.
ANNs consist of so-called neurons, which are arranged in several layers and connected to each other in different ways. The arrangement and connection of the neurons creates different types of neural networks such as feedforward networks, recurrent networks or convolutional neural networks.
Neural networks form the basis of artificial intelligence (AI) and machine learning (ML). The networks can be trained through unsupervised or supervised learning and are able to solve problems from different areas using computers. Typical applications include text, speech and image recognition, data analysis, statistical evaluations and much more.
The Structure of a Neural Network
Put simply, the abstract mathematical model of a neural network consists of neurons that are arranged in different layers and linked to one another in France Phone Number List different ways. The neurons are also referred to as nodes or units. A neuron is able to receive information at its input from outside or from another neuron, evaluate the information in a certain way and pass it on in a modified form at the neuron output to another neuron or output it as the final result.
Basically, it is possible to distinguish between the neuron types input neurons, hidden neurons and output neurons. Input neurons form the entrance of an artificial neural network and receive information from the outside world. Hidden neurons are arranged between the input neurons and output neurons. Depending on the network type, there can be several layers of hidden neurons. They ensure that the information is forwarded and processed. Output neurons ultimately provide a result and output it to the outside world.
The connections between the neurons are also called edges
Edges connect the output of one neuron with the input of another neuron. The edges can be weighted according to their importance. The more heavily an edge is weighted, the greater the influence that the output signal of one neuron has on another neuron. In principle, a distinction can be made between positive and negative weightings. Accordingly, the weighting has an stimulating or inhibitory influence on the connected neuron.
A weight of zero represents a neutral neuron Cell Phone Number Database connection. The knowledge and problem-solving behavior of a neural network is stored in the edges, their weights and the threshold values or processing rules mapped to the nodes (neurons). The ability to solve complex problems depends on the number of neurons, the neuron layers present and the type of networking of the neurons.
When a neural network is trained, the weighting of the connections changes. Learning is based on predefined learning rules and the results achieved. The more neurons and connections there are in an artificial neural network, the more computing power is required for the learning processes and operation of the network.
Types of Neural Networks
Neural networks exist in many different structures and levels of complexity. Some of the most important basic types of neural networks are Bulk Database discussed below. In principle, a distinction can be made between the simple perceptron, feedforward networks, recurrent networks and convolutional neural networks.
The simplest form of a neural network is the perceptron . It consists of a single neuron that converts input information into output information depending on a weighting and a threshold value. The term perceptron is also sometimes used for single-layer feedforward networks.
In a feedforward network, information always flows in a forward direction. The flow of information starts at the input neurons. The information is passed on to the output neurons via one or more layers of hidden neurons, which ultimately deliver the result.
