Which term best describes the entire model composed of layers, weights, and activations used to model complex patterns?

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Multiple Choice

Which term best describes the entire model composed of layers, weights, and activations used to model complex patterns?

An artificial neural network describes the whole model built from multiple layers of neurons, with weighted connections and activation functions. The weights determine how strongly each input influences the next layer, while activations introduce nonlinearity so the network can learn complex, non-obvious patterns. Stacking layers—especially with nonlinear activations—lets the model build hierarchical representations, from simple features to intricate abstractions. Other options don’t fit this description: a decision tree is a sequence of split rules, linear regression is a single linear relationship, and K-means is a clustering method. This combination of layered structure, weights, and activations together is what makes an artificial neural network the right term.

An artificial neural network describes the whole model built from multiple layers of neurons, with weighted connections and activation functions. The weights determine how strongly each input influences the next layer, while activations introduce nonlinearity so the network can learn complex, non-obvious patterns. Stacking layers—especially with nonlinear activations—lets the model build hierarchical representations, from simple features to intricate abstractions. Other options don’t fit this description: a decision tree is a sequence of split rules, linear regression is a single linear relationship, and K-means is a clustering method. This combination of layered structure, weights, and activations together is what makes an artificial neural network the right term.

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