Optimining through Deep Learning

Architex aims to make its new theoretical concept of "Optimining" possible through the Morpheus protocol.

Convolutional Neural Networks (CNNs)

Application: Real-time analysis of cryptocurrency market trends to predict mining difficulty and price fluctuations.

Operation: CNNs process historical market data time series to identify patterns that may indicate future trends.

Basic Formula: The output y is a function of the inputs x with a set of weights and a bias b, y = f(w * x + b).

Recurrent Neural Networks (RNNs)

Application: Predicting mining performance based on historical operational data and current network conditions.

Operation: RNNs use their ability to maintain a 'state' or memory of previous inputs to predict future output based on a sequence of input data.

Basic Formula: For a sequence of inputs x1, x2, ..., xn, the hidden state ht is updated at each time step t, ht = f(whh * ht-1 + wxh * xt + b).

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Architex Labs - 2024