Neural Network

A neural network is a model of machine learning that can recognize patterns, make predictions and solve problems. Its structure is modeled after the human brain.

It also forms the basis for deep learning and is used where conventional algorithms reach their limits.

Functionality ans Usage

An artificial neural network consists of nodes (neurons) connected to each other and is organized in different layers. The connections between the neurons can be weighted to control the influence of individual signals. In addition, there are so-called distortions (bias) that influence the decision threshold of a neuron.

The input layer receives the raw data and processes the meaning of the individual signals, while the hidden layers further process this data and understand the context. If a network consists of many layers, it is called a deep neural network. After processing, the output layer delivers the result. Essentially, the network learns how to convert raw data into meaningful patterns and use them for predictions.

Examples

• Faces are recognized in pictures
• Images are classified by animal species
• Language is recognized and converted into text
• Recommendations are created based on user behavior