A neural network is a computing system inspired by the structure and functioning of the human brain. Made up of interconnected nodes (analogous to neurons), these networks are designed to simulate the way humans learn and process information. Here's an overview of their structure and functionality:
- Nodes (Neurons): Each node in a neural network represents an artificial neuron. These nodes are interconnected and communicate with each other, much like neurons in the brain.
- Layers: Neural networks are structured in layers: an input layer, one or more hidden layers, and an output layer. Each layer contains a number of nodes, and the complexity of the network can vary depending on the number of layers and nodes within them.
- Learning Process: Neural networks learn through a process involving adjusting the weights of connections between nodes. This learning is typically achieved using algorithms like backpropagation, combined with training data.
- Applications: Neural networks are widely used in various applications, including image and speech recognition, language processing, game playing, medical diagnosis, and many other areas of artificial intelligence.
The flexibility and learning capability of neural networks make them a powerful tool in machine learning and AI, enabling them to handle complex and diverse tasks, often surpassing traditional computing approaches in these domains.