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Neural networks, inspired by the human brain, are central to enterprise AI. They process data through interconnected layers that identify complex patterns and enable accurate decisions. There are several types of networks, from simple single-layer networks for basic problems to deep networks (DNNs), which address advanced tasks such as image and text processing. Their applications are broad: in finance, they detect fraud and assess credit risks; in marketing, they personalize campaigns and predict consumer behavior; and in logistics, they optimize routes and supply chains. However, the high computational cost and the need for massive data represent significant technical challenges.
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Neural networks, inspired by the human brain, are central to enterprise AI. They process data through interconnected layers that identify complex patterns and enable accurate decisions. There are several types of networks, from simple single-layer networks for basic problems to deep networks (DNNs), which address advanced tasks such as image and text processing. Their applications are broad: in finance, they detect fraud and assess credit risks; in marketing, they personalize campaigns and predict consumer behavior; and in logistics, they optimize routes and supply chains. However, the high computational cost and the need for massive data represent significant technical challenges.