Translations:Neural Networks/28/en: Difference between revisions

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    * '''[[Convolutional Neural Networks]]''' (CNNs) — designed for grid-structured data such as images, using local connectivity and weight sharing.
    * '''[[Convolutional Neural Networks]]''' (CNNs) — designed for grid-structured data such as images, using local connectivity and weight sharing.
    * '''[[Recurrent Neural Networks]]''' (RNNs) — designed for sequential data, with connections that form cycles to maintain hidden state.
    * '''[[Recurrent Neural Networks]]''' (RNNs) — designed for sequential data, with connections that form cycles to maintain hidden state.
    * '''{{Term|transformer|Transformers}}''' — {{Term|attention}}-based architectures that have become dominant in natural language processing and increasingly in vision.
    * '''Transformers''' — attention-based architectures that have become dominant in natural language processing and increasingly in vision.
    * '''Autoencoders''' — networks trained to reconstruct their input, used for dimensionality reduction and generative modelling.
    * '''Autoencoders''' — networks trained to reconstruct their input, used for dimensionality reduction and generative modelling.
    * '''Generative adversarial networks''' (GANs) — pairs of networks (generator and discriminator) trained in competition to generate realistic data.
    * '''Generative adversarial networks''' (GANs) — pairs of networks (generator and discriminator) trained in competition to generate realistic data.

    Revision as of 22:01, 27 April 2026

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    Message definition (Neural Networks)
    * '''[[Convolutional Neural Networks]]''' (CNNs) — designed for grid-structured data such as images, using local connectivity and weight sharing.
    * '''[[Recurrent Neural Networks]]''' (RNNs) — designed for sequential data, with connections that form cycles to maintain hidden state.
    * '''{{Term|transformer|Transformers}}''' — {{Term|attention}}-based architectures that have become dominant in natural language processing and increasingly in vision.
    * '''Autoencoders''' — networks trained to reconstruct their input, used for dimensionality reduction and generative modelling.
    * '''Generative adversarial networks''' (GANs) — pairs of networks (generator and discriminator) trained in competition to generate realistic data.
    • Convolutional Neural Networks (CNNs) — designed for grid-structured data such as images, using local connectivity and weight sharing.
    • Recurrent Neural Networks (RNNs) — designed for sequential data, with connections that form cycles to maintain hidden state.
    • Transformers — attention-based architectures that have become dominant in natural language processing and increasingly in vision.
    • Autoencoders — networks trained to reconstruct their input, used for dimensionality reduction and generative modelling.
    • Generative adversarial networks (GANs) — pairs of networks (generator and discriminator) trained in competition to generate realistic data.
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