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redx94 / Dynamic-Neural-Network-Refinement

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Dynamic Neural Network Refinement (DNNR) is an advanced framework that allows neural networks to adapt in real time. Unlike static systems, DNNR refines network parameters on-the-fly to optimize performance. Its modularity ensures easy customization for versatile applications.

machine-learning deep-learning artificial-intelligence neural-networks intelligent-systems performance-optimization adaptive-systems machine-learning-framework data-driven-ai customizable-ai real-time-optimization ai-scalability on-the-fly-refinement network-parameter-tuning versatile-ai-solutions advanced-neural-architectures dynamic-learning-models ai-research-and-development
  • Updated Jan 28, 2025
  • Python

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