Närvivõrkude

Närvivõrkude, also known as neural networks, are a class of machine learning algorithms inspired by the structure and function of biological neural networks. They consist of interconnected nodes or "neurons" organized in layers. Each connection between neurons has an associated weight, which is adjusted during the training process to learn patterns in data.

The basic structure of a neural network typically includes an input layer, one or more hidden layers, and an output layer. The input layer receives the raw data, which is then processed through the hidden layers. Each neuron in a hidden layer performs a computation based on the weighted sum of its inputs and applies an activation function to produce an output. This output is then passed to the next layer. The output layer produces the final result, which could be a classification, a prediction, or some other form of output depending on the task.

Training a neural network involves feeding it large amounts of labeled data. During training, the network makes predictions, and an error function calculates the difference between the predicted output and the actual target. This error is then used to update the weights of the connections through an optimization algorithm, such as backpropagation, in an iterative process. The goal is to minimize the error, thereby improving the network's accuracy.

Neural networks have proven highly effective in a wide range of applications, including image recognition, natural language processing, speech synthesis, and recommendation systems. Their ability to learn complex, non-linear relationships in data makes them a powerful tool in artificial intelligence and data science. Different architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are specialized for particular types of data and tasks.