Transformer Model for Language Understanding Part 01 - DLMC47

The core idea behind the Transformer model is self-attention—the ability to attend to different positions of the input sequence to compute a representation of that sequence. Transformer creates stacks of self-attention layers and is explained below in the sections Scaled dot product attention and Multi-head attention.

Mar 30, 2021, 4:00 – 5:00 PM

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Machine Learning

About this event

The core idea behind the Transformer model is self-attention—the ability to attend to different positions of the input sequence to compute a representation of that sequence. Transformer creates stacks of self-attention layers and is explained below in the sections Scaled dot product attention and Multi-head attention.

Speaker

  • Muhammad Huzaifa Shahbaz

    Lenaar Digital

    Co-founder

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Amal4Ajar

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IEEE Computer Society SSUET

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  • Laiba Rafiq

    GDSC Lead

  • Maaz Farman

    SPARK⚡BIZ

    Community Mentor

  • shayan faiz

    techrics

    Outreach Coordinator

  • Muhammad Ahmer Zubair

    Sharp Edge

    Media Creative Lead

  • Ehtisham Ul Haq

    Tech Lead

  • Mohammad Nabeel Sohail

    AI and Chatbot Developer | Full Stack Web | PAFLA Ambassador | Public Speaker | Trainer

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  • Kashan Khan Ghori

    Softseek International

    Operations Lead

  • Daniyal Jamil

    Technology Links

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  • Sami Faiz Qureshi

    ConciSafe

    Event Management Lead

  • Maham Amjad

    Content Writing Lead

  • Syed Affan Hussain

    HnH Soft Tech Solutions Pvt Ltd

    Host

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