Neural Network

A neural network is a computing system loosely inspired by the human brain, consisting of interconnected nodes (artificial neurons) organized in layers. Information flows through the network, with each connection having a learnable weight that determines its importance.

Neural network structure: input layer (receives data), hidden layers (process information — "deep" networks have many hidden layers), output layer (produces the result), weights (learnable parameters on connections), and activation functions (introduce non-linearity, enabling complex pattern learning).

How neural networks learn: forward pass (data flows through the network to produce output), loss calculation (comparing output to desired answer), backpropagation (calculating how each weight contributed to the error), and weight update (adjusting weights to reduce error). This cycle repeats millions of times during training.

Types of neural networks: feedforward networks (simplest — data flows one direction), convolutional networks (CNNs — specialized for images and spatial data), recurrent networks (RNNs — specialized for sequential data), transformers (the architecture behind modern LLMs), and graph neural networks (for relationship data).

Neural networks are the building blocks of modern AI. Every major AI application — from ChatGPT to self-driving cars to medical imaging — is powered by neural networks. Understanding the basic concept (layers of nodes learning patterns from data) provides useful context for working with AI systems.