What Is Neural Network?
A neural network is a computational model inspired by the structure of the human brain, consisting of interconnected layers of nodes (neurons) that process data by learning patterns through adjusting the strength of connections (weights) during training.
How Neural Network Works
Neural networks are the building blocks of modern AI. They consist of an input layer, one or more hidden layers, and an output layer. Each connection between neurons has a weight that is adjusted during training to minimize prediction errors. Simple neural networks with a few layers can learn basic patterns, while deep neural networks with many layers (deep learning) can learn complex, hierarchical representations. Different architectures are suited for different tasks: CNNs for images, RNNs for sequences, and Transformers for language. Neural networks have enabled breakthroughs in image recognition, language understanding, game playing, protein structure prediction, and virtually every area of AI.
Real-World Examples
A neural network trained on millions of photos learning to recognize faces with superhuman accuracy
A three-layer neural network classifying handwritten digits by learning to detect edges, shapes, and digit patterns
AlphaFold using a neural network to predict protein structures, solving a 50-year biology grand challenge