A neural network is a computing system inspired by the structure and function of the human brain. In artificial intelligence and machine learning, neural networks are made up of layers of interconnected nodes, often called neurons. Each neuron takes in one or more inputs, applies a mathematical operation (often a weighted sum followed by an activation function), and passes the result to the next layer. The basic architecture usually consists of an input [layer](https://thealgorithmdaily.com/input-layer), one or more hidden layers, and an output layer.
Neural networks are used for a wide range of tasks such as image recognition, natural language processing, and game playing. During training, the network learns to adjust the weights and biases of its connections to minimize the difference between its predictions and the actual results. This is typically achieved using an algorithm called gradient descent, which incrementally updates the network’s parameters to reduce a loss function.
One of the key strengths of neural networks is their ability to model complex, non-linear relationships in data. Unlike traditional linear models, neural networks can capture intricate patterns and interactions by stacking multiple layers of transformation. The depth (number of layers) and width (number of neurons per layer) can be tuned based on the complexity of the problem and the amount of available data.
Neural networks come in many forms. A simple feedforward neural network processes data in one direction, from input to output. More advanced architectures include convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) for sequence data like text or time series. These specialized networks apply constraints or structures that make them particularly effective for certain types of tasks.
Training a neural network requires a dataset (often called a training set), a loss function to measure error, and an optimization algorithm. The process involves forward propagation (calculating predictions) and backpropagation (updating weights based on errors). While neural networks can achieve impressive accuracy, they also require large amounts of data and computational resources. Overfitting, where the network learns noise instead of signal, is a common challenge; regularization techniques help address this.
Neural networks are at the core of many modern AI applications, from speech recognition to self-driving cars. They have rapidly advanced fields like deep learning, where networks with many hidden layers (deep neural networks) achieve state-of-the-art results on complex problems. As research continues, new architectures and training techniques are pushing the boundaries of what neural networks can do.
