Model Components
Artificial Neural Network
ANNs are composed of input, hidden, and output layers of artificial neurons (nodes) connected by weighted edges. Signals pass through the network via forward propagation; weights are updated via backpropagation to minimize prediction error on training data.
ANNs are universal function approximators: given sufficient size and data, they can approximate any continuous function. This theoretical property, combined with GPU hardware and large datasets, has made them the dominant approach in modern AI.
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Related Terms
Model Components
Neural Network
Computational system of interconnected nodes inspired by the human brain that learns to recognize patterns.
Core Concepts
Deep Learning
Subset of ML using neural networks with many layers to analyze complex data representations.
Techniques & Methods
Backpropagation
Training algorithm that adjusts neural network weights by propagating prediction errors backward through the network.
Model Components
Parameter
A learnable variable within a model whose value is adjusted during training to minimize prediction error.