A neural network is a computational model made up of layers of interconnected nodes (often called neurons). Each connection has an associated weight, and during training these weights are adjusted so the network can map inputs to desired outputs, allowing it to capture complex, non-linear relationships. Linear regression (straight-line fitting), tokenization of text, and specialized hardware chips may all play roles in ML workflows, but none of those are themselves learning models built from layered, weighted connections.
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Fundamentals of AI and ML
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