Convolutional Neural Networks (CNN)
CNN excels in identifying patterns in spatial or temporal data. It can be used to detect patterns in stock price movements.
Under the conditions with the same data size and complexity, usually CNN has lower computational costs compared with LSTM.
Long Short-term Memory (LSTM)
LSTM can study long-term dependencies, making it suitable for time series data such as stock prices.
The "memory cell" structure enables it to remember long-term dependencies and address the "vanishing gradient" problem that often encountered in traditional recurrent networks.
Bidirectional LSTM (Bi-LSTM)
Bi-LSTM is an extension of LSTM, formed by combining forward LSTM and backward LSTM.
Like LSTM, Bi-LSTM can remember long-term dependencies. Besides than that, Bi-LSTM can learn from future states.
By gathering information from both the past and future, Bi-LSTM makes better contextual judgments, making the spatial model more comprehensive.
Converting Supernodes into NFTs
Reputation of supernodes
Conversion of supernodes into NFTs
Ownership of NFTs
Aligning with the goals of Web3
DBC Distributed Computing Power
Sharing computing resources (GPU power)
Enhancing data security and privacy
Facilitating value exchange
Collaborative development of AI applications
Utility Token of AI industry: VNX
Global trading service for data, algorithms, and solutions in the AI industry
Investment asset that users can buy and hold
Cross-border remittance with faster transfer speed and lower fees facilitated by blockchain technology
Enjoying more freedom and transparency in financial services within the Web3 environment