AGIX Whitepaper
  • 📃Overview
    • Introduction
    • Mission
    • Vision
    • Values
    • AI Fundamentals
      • The Journey to Integrated AI
      • Types of Artificial Intelligence
      • Natural Language Processing (NLP)
      • Large Language Models (LLM)
      • Text to Image Modules (TTIMs)
      • Machine Learning
      • Transformer Architecture
      • Pretrained Language Model
      • Generative Model
      • Fine-Tuning
      • Tokenization
      • Contextual Awareness
  • 🤖AGIX Ecosystem
    • Decentralized Marketplace of AI Agents
    • Community Management AI Agent
      • Community AI Agent Features
      • Special Features for Admins
      • How to Set Up the Community AI Agent
      • Pricing Tiers
    • Personal Trading AI Agent
      • Trading AI Agent Features
    • Support AI Agent For Blockchains
      • Case Study
      • Order Custom AI Agent
    • WebApp
      • Conversations Module
      • AI Decentralized Exchange
      • Analytics Dashboard
      • My AIgents (Setup Dashboard)
      • NFT Generation
      • Staking Dashboard
      • Affiliate Program
      • Team Verification
    • Extension – Smart Browsing Tool
    • Platform Integrations
  • 🤝Collaboration
    • Ecosystem Partners
    • Marketing Opportunity
  • 📊Tokenomics
    • $AGX Token
    • Token Distribution
    • How to Buy $AGX Token
  • 🔗Socials
    • Twitter
    • Telegram Channel
    • Telegram Chat
  • 🔍Legal & Terms
    • Privacy Policy
    • Terms & Conditions
    • $AGX Token Disclaimer
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  1. Overview
  2. AI Fundamentals

Transformer Architecture

Transformer architecture represents an innovative neural network structure developed by Google's AI division, Google Brain. This approach surpasses the previous Recurrent Neural Networks (RNN) model by enabling parallel data processing, allowing it to handle large blocks of information more efficiently than the sequential processing of RNNs.

At its core, transformer architecture focuses on pinpointing key elements within data through precise attention allocation, thus minimizing unnecessary computations. It incorporates four main components: Attention Mechanisms, Multi-head Attention, Feed-Forward Layers, and Normalization Layers, each contributing to its ability to manage and interpret data effectively.

AGIX incorporates transformer architecture to enhance its AI capabilities, offering users the flexibility to submit extensive input requests while ensuring accurate and timely responses.

PreviousMachine LearningNextPretrained Language Model

Last updated 7 months ago

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