๐Ÿ”ฌ
Minerva AI (EN)
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  • ๐Ÿ‘‹Welcome to Minerva AI
  • STUDIES & FINDINGS
    • โ˜๏ธAbstract
    • ๐Ÿ“Introduction and related works
    • ๐Ÿ’กModel overview
      • โ™พ๏ธConvolutional neural network - CNN
      • โœ๏ธDropout techniques
      • ๐Ÿง Long short-term memory (LSTM)
      • ๐Ÿ™†Methodology
      • ๐Ÿ”—Data collection and merging
      • ๐Ÿ› ๏ธModel architecture
      • โž•Dataset and Parameter Optimization
      • ๐Ÿ‘ŒResults and discussion
      • โš“Trading Philosophy & Method
      • โž—Basic algorithm
      • ๐ŸŒ Results
    • ๐Ÿ‘‰Conclusion
  • PRODUCT DEVELOPMENT
    • ๐Ÿ‘€Vision and development roadmap
    • ๐ŸŒŸRevenue models
    • ๐Ÿช™Tokenomics
  • TEAM
    • ๐Ÿ‘ฅFounding team
  • RESOURCES
    • ๐Ÿ“—References
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  1. STUDIES & FINDINGS

๐Ÿ’กModel 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

โ™พ๏ธConvolutional neural network - CNNโœ๏ธDropout techniques๐Ÿง Long short-term memory (LSTM)๐Ÿ™†Methodology๐Ÿ”—Data collection and merging๐Ÿ› ๏ธModel architectureโž•Dataset and Parameter Optimization๐Ÿ‘ŒResults and discussionโš“Trading Philosophy & Methodโž—Basic algorithm๐ŸŒ Results

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Last updated 1 year ago