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Minerva AI (EN)
  • πŸ‘‹Welcome to Minerva AI
  • STUDIES & FINDINGS
    • ☝️Abstract
    • πŸ“Introduction and related works
    • πŸ’‘Model overview
      • ♾️Convolutional neural network - CNN
      • ✍️Dropout techniques
      • 🧠Long short-term memory (LSTM)
      • πŸ™†Methodology
      • πŸ”—Data collection and merging
      • πŸ› οΈModel architecture
      • βž•Dataset and Parameter Optimization
      • πŸ‘ŒResults and discussion
      • βš“Trading Philosophy & Method
      • βž—Basic algorithm
      • 🌠Results
    • πŸ‘‰Conclusion
  • PRODUCT DEVELOPMENT
    • πŸ‘€Vision and development roadmap
    • 🌟Revenue models
    • πŸͺ™Tokenomics
  • TEAM
    • πŸ‘₯Founding team
  • RESOURCES
    • πŸ“—References
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  1. STUDIES & FINDINGS

πŸ’‘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)πŸ™†MethodologyπŸ”—Data collection and mergingπŸ› οΈModel architectureβž•Dataset and Parameter OptimizationπŸ‘ŒResults and discussionβš“Trading Philosophy & Methodβž—Basic algorithm🌠Results

Get Started

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Last updated 1 year ago