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  1. NEURAL Definition & Meaning - Merriam-Webster

    The meaning of NEURAL is of, relating to, or affecting a nerve or the nervous system. How to use neural in a sentence.

  2. Neural DSP - Algorithmically Perfect

    Soldano SLO-100 X is compatible with Quad Cortex. Purchase a license and log in to your Neural DSP account on your Quad Cortex to unlock it.

  3. NEURAL | English meaning - Cambridge Dictionary

    NEURAL definition: 1. involving a nerve or the system of nerves that includes the brain: 2. involving a nerve or the…. Learn more.

  4. Neural network - Wikipedia

    Neural networks are used to solve problems in artificial intelligence, and have thereby found applications in many disciplines, including predictive modeling, adaptive control, facial recognition, handwriting …

  5. neural, adj. & n. meanings, etymology and more | Oxford ...

    neural, adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary

  6. Neural - definition of neural by The Free Dictionary

    Define neural. neural synonyms, neural pronunciation, neural translation, English dictionary definition of neural. adj. 1. Of or relating to a nerve or the nervous system.

  7. What is a neural network? - IBM

    Neural networks allow programs to recognize patterns and solve common problems in artificial intelligence, machine learning and deep learning.

  8. neural adjective - Definition, pictures, pronunciation and ...

    Definition of neural adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.

  9. What is a Neural Network? - GeeksforGeeks

    Dec 16, 2025 · Neural networks are machine learning models that mimic the complex functions of the human brain. These models consist of interconnected nodes or neurons that process data, learn …

  10. What is a Neural Network? - Artificial Neural Network ...

    Neural networks can help computers make intelligent decisions with limited human assistance. This is because they can learn and model the relationships between input and output data that are nonlinear …