Neuraalvõrkudel

Neuraalvõrkudel, also known as artificial neural networks, are computational models inspired by the structure and function of biological neural networks in the human brain. They are a core component of machine learning and artificial intelligence, designed to recognize patterns and make predictions. A neural network consists of interconnected nodes, or artificial neurons, organized in layers. The most common architecture includes an input layer, one or more hidden layers, and an output layer.

Information enters the network through the input layer. Each neuron in the network receives input signals, processes them using an activation function, and transmits an output signal to other neurons. These connections between neurons have associated weights, which are adjusted during the training process. Training involves feeding the network a large dataset and modifying the weights to minimize the difference between the network's predictions and the actual outcomes. This iterative process allows the network to learn complex relationships within the data.

Neural networks are used in a wide range of applications, including image recognition, natural language processing, speech synthesis, and recommendation systems. Different types of neural networks exist, such as convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data. Their ability to learn from data and adapt makes them powerful tools for solving complex problems that are difficult to address with traditional algorithms.