This project aims to implement an innovative Proof-of-Work (PoW) algorithm for cryptocurrency mining by leveraging artificial intelligence, specifically a Feedforward Neural Network (FNN). The primary goal is to train the FNN to produce accurate outputs based on the provided input data. The difficulty of the mining process can be adjusted by modifying the target error for the neural network.

• Implements a simple Feedforward Neural Network (FNN) for the PoW algorithm.
• Trains the neural network to produce accurate outputs based on the provided input data.
• Controls the difficulty of the mining process by adjusting the target error.
• Utilizes the Gonum library for matrix operations to efficiently handle the neural network computations.
• Provides a modular and flexible design for easy extension to other neural network architectures.

• InputData: A struct representing the input data used for training the neural network.
• NeuralNetwork: A struct representing the neural network, which is composed of multiple layers.
• Layer: A struct representing a layer within the neural network, including its weights and biases.
• PoWFNN: The main function that trains the neural network as a PoW algorithm, adjusting the target error to control the difficulty of the mining process.
• The PoW algorithm is executed by training the neural network to produce the correct outputs based on the input data provided. The difficulty is controlled by adjusting the target error, and the mining process is considered complete when the target error is met.

• Cryptocurrency mining: This project can be used as a PoW algorithm for new cryptocurrencies that seek to utilize AI-based mining processes.
• Incentivizing AI research: By incorporating AI model training into the mining process, this project can encourage further research and development in the AI domain.
• Energy-efficient mining: AI-based PoW algorithms may lead to more energy-efficient mining operations compared to traditional PoW algorithms.

• Support for other neural network architectures, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer models.
• Dynamic adjustment of the neural network's complexity based on network hashrate and target block time.
• Implementation of a reward mechanism to incentivize miners to contribute to the training of more sophisticated AI models.