Neural Network

A Neural Network is a computational model loosely inspired by the brain, made of interconnected layers of nodes ("neurons") that learn from data by adjusting the strengths of their connections. Each neuron takes weighted inputs, applies an activation function, and passes the result forward. Networks range from tiny three-layer classifiers to massive transformers with billions of parameters. Real-world examples include the convolutional neural networks behind iPhone face recognition, the recurrent networks that powered early machine translation, the transformer networks behind ChatGPT and Gemini, and the graph neural networks used in drug discovery. Frameworks like PyTorch, TensorFlow, and Keras have made building neural networks accessible to mainstream developers. Neural networks underpin almost every modern AI breakthrough. Because they can be opaque, AI governance frameworks demand explainability, model cards, and AI risk management controls before deployment. Understanding neural networks is foundational to AI compliance, AI ethics, and responsible AI in enterprise AI environments.

Centralpoint Wraps Every Neural Network in Governance: Whatever neural network powers your AI — ChatGPT, Gemini, Llama, or an on-premise embedded model — Centralpoint by Oxcyon governs it. The platform meters every LLM call, stores prompts and skills inside your firewall, and lets you launch as many chatbots as your organisation needs via a single line of JavaScript.


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