techniques

Neural Network

Computing systems inspired by biological neural networks in the human brain.

Definition

Neural networks are a series of algorithms that recognize underlying relationships in data through a process that mimics how the human brain operates.

**Structure:**
Input Layer: Receives raw data
Hidden Layers: Process and transform data
Output Layer: Produces final result

How They Learn: 1. Data flows through the network 2. Each neuron applies weights and activation functions 3. Output is compared to expected result 4. Errors propagate backward (backpropagation) 5. Weights are adjusted to minimize error

Examples

Image recognition in photos, voice recognition in smart speakers.

Related Terms

Deep Learning

Machine learning using neural networks with many layers to learn complex patterns.

Activation Function

Mathematical functions that determine if a neuron should fire based on input.

Backpropagation

Algorithm for training neural networks by propagating errors backward through layers.

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