Neuraaliverkko

Neuraaliverkko, also known as an artificial neural network (ANN), is a computational model inspired by the structure and function of biological neural networks found in animal brains. These networks are composed of interconnected nodes, often called neurons, that process and transmit information. Each connection between neurons has a weight, which can be adjusted during a learning process.

The fundamental unit of a neural network is the artificial neuron. It receives inputs, applies a mathematical function to these inputs, and produces an output. This output is then passed to other neurons. The neurons are typically organized in layers: an input layer, one or more hidden layers, and an output layer. The input layer receives the raw data, while the hidden layers perform complex computations, and the output layer provides the final result.

Neural networks learn by being trained on data. During training, the network is presented with a set of examples, and its internal weights are adjusted to minimize the difference between its predicted output and the actual desired output. This process, often referred to as backpropagation, allows the network to identify patterns and relationships within the data.

Once trained, neural networks can be used for a variety of tasks, including image recognition, natural language processing, forecasting, and decision-making. Their ability to learn from data and adapt makes them powerful tools in the field of artificial intelligence and machine learning. The complexity and capabilities of a neural network depend on its architecture, the number of neurons and layers, and the training data.