AI Reference 031 Neural Networks & Deep Learning Wikipedia guided Primary sources linked

Artificial Neural Networks

Definition

Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function.

What is an artificial neural network?

An artificial neural network is a parameterized mathematical model built from layers of connected units. Each connection has a numerical weight, and the network transforms input values through repeated weighted sums and nonlinear activation functions.

Where the idea came from

Early neural models include the 1943 McCulloch–Pitts neuron and Rosenblatt's perceptron in the 1950s. Modern neural networks differ enormously in scale and architecture, but they retain the basic idea that behavior can be produced by many simple numerical operations whose parameters are learned from data.

What training changes

Training does not usually insert explicit rules into the network. An optimizer changes millions or billions of parameters so the network's outputs reduce a chosen loss function. The resulting knowledge is distributed across numerical weights rather than stored as a readable list of facts.

Related terms, defined

Neural Networks & Deep Learning Backpropagation Backpropagation efficiently computes gradients through differentiable neural networks by applying the chain rule from outputs back toward earlier layers. Neural Networks & Deep Learning Convolutional Neural Networks Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function. Neural Networks & Deep Learning Recurrent Neural Networks Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function. Neural Networks & Deep Learning Long Short-Term Memory Networks LSTM networks are recurrent neural networks with gated memory mechanisms designed to preserve and control information across longer sequences. Neural Networks & Deep Learning Word Embeddings Word embeddings represent words or tokens as dense numerical vectors whose geometry captures statistical relationships learned from data.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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