Neural networks are computational models inspired by the human brain’s structure, consisting of interconnected nodes (neurons) organized in layers. These models process data through these layers, enabling machines to recognize patterns, make decisions, and learn from experience.
In a neural network, each neuron receives inputs, processes them using a mathematical function, and passes the output to subsequent neurons. This architecture allows neural networks to handle complex tasks such as image and speech recognition, natural language processing, and predictive analytics.
The learning process in neural networks involves adjusting the weights of connections between neurons based on the error of the network’s predictions. This adjustment is typically achieved through algorithms like backpropagation, enabling the network to improve its performance over time.
« Back to Glossary Index