Neural Network
A computing system inspired by the biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information and learn to recognize patterns.
Why It Matters
Neural networks are the backbone of modern AI — from voice assistants to autonomous vehicles. Understanding them is key to understanding how AI 'thinks'.
Example
A neural network trained on thousands of cat and dog photos learns to distinguish between them by recognizing features like ear shape, fur texture, and body proportions.
Think of it like...
Like a company org chart where information flows from junior employees (input layer) through middle managers (hidden layers) to executives (output layer), with each level adding more insight.
Related Terms
Deep Learning
A specialized subset of machine learning that uses artificial neural networks with multiple layers (hence 'deep') to learn complex patterns in data. Deep learning excels at tasks like image recognition, speech processing, and natural language understanding.
Perceptron
The simplest form of a neural network — a single neuron that takes weighted inputs, sums them, and applies an activation function to produce an output. It is the fundamental building block of neural networks.
Activation Function
A mathematical function applied to the output of each neuron in a neural network that introduces non-linearity. Without activation functions, a neural network would just be a series of linear transformations.
Backpropagation
The primary algorithm used to train neural networks. It calculates how much each weight in the network contributed to the error, then adjusts weights backward from the output layer to reduce future errors.