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Minerva AI (EN)
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  • 👋Welcome to Minerva AI
  • STUDIES & FINDINGS
    • ☝️Abstract
    • 📍Introduction and related works
    • 💡Model overview
      • ♾️Convolutional neural network - CNN
      • ✍️Dropout techniques
      • 🧠Long short-term memory (LSTM)
      • 🙆Methodology
      • 🔗Data collection and merging
      • 🛠️Model architecture
      • ➕Dataset and Parameter Optimization
      • 👌Results and discussion
      • ⚓Trading Philosophy & Method
      • ➗Basic algorithm
      • 🌠Results
    • 👉Conclusion
  • PRODUCT DEVELOPMENT
    • 👀Vision and development roadmap
    • 🌟Revenue models
    • 🪙Tokenomics
  • TEAM
    • 👥Founding team
  • RESOURCES
    • 📗References
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  1. STUDIES & FINDINGS

💡Model overview

In this paper, we utilized a combination of CNN and LSTM layers. Therefore, before presenting our methodology, we will introduce the concepts of CNN and LSTM.

Our findings

♾️Convolutional neural network - CNN✍️Dropout techniques🧠Long short-term memory (LSTM)🙆Methodology🔗Data collection and merging🛠️Model architecture➕Dataset and Parameter Optimization👌Results and discussion⚓Trading Philosophy & Method➗Basic algorithm🌠Results

Get Started

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Last updated 2 years ago