tx · BiEEP1Fkm3mKRRA89ugbNMX1gES6cJAAs4R6a3Fuia2H
3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL: -0.00500000 Waves
2023.08.17 12:35 [2714430] invoke 3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL > 3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV commitTask()
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: checked_out_by_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_chatgpt_5YfeP1pkw1savCkwNPDWdY3dDF2BRnNwiQC2WmCWybK5_D2Eu5TQSVkAwDi7WKia7vFRkw5diyFYkCRhRzsVDmjQp: true -> null
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 5YfeP1pkw1savCkwNPDWdY3dDF2BRnNwiQC2WmCWybK5_D2Eu5TQSVkAwDi7WKia7vFRkw5diyFYkCRhRzsVDmjQp_commit_timestamp: 1692264955704
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 5YfeP1pkw1savCkwNPDWdY3dDF2BRnNwiQC2WmCWybK5_D2Eu5TQSVkAwDi7WKia7vFRkw5diyFYkCRhRzsVDmjQp_commit_height: 2714430
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 5YfeP1pkw1savCkwNPDWdY3dDF2BRnNwiQC2WmCWybK5_D2Eu5TQSVkAwDi7WKia7vFRkw5diyFYkCRhRzsVDmjQp_result: "Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain. They consist of interconnected nodes, called neurons, that process and transmit information. Neural networks are used for tasks such as pattern recognition, classification, and regression.
In a neural network, information is passed through multiple layers of neurons. Each neuron receives inputs, applies an activation function to those inputs, and then passes the output to the next layer. This process continues until the final layer produces the desired output.
Neural networks learn by adjusting the strength of connections between neurons, known as weights, based on the input data. This training process helps the network improve its accuracy and make predictions on new, unseen data.
There are different types of neural networks, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and others. Each type is suited for different types of data and tasks."
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 5YfeP1pkw1savCkwNPDWdY3dDF2BRnNwiQC2WmCWybK5_D2Eu5TQSVkAwDi7WKia7vFRkw5diyFYkCRhRzsVDmjQp_status: "checked_out" -> "done"
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"timestamp": 1692265040086,
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"sender": "3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL",
"senderPublicKey": "92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97",
"proofs": [
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{
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"value": "5YfeP1pkw1savCkwNPDWdY3dDF2BRnNwiQC2WmCWybK5_D2Eu5TQSVkAwDi7WKia7vFRkw5diyFYkCRhRzsVDmjQp"
},
{
"type": "string",
"value": "Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain. They consist of interconnected nodes, called neurons, that process and transmit information. Neural networks are used for tasks such as pattern recognition, classification, and regression.\n\nIn a neural network, information is passed through multiple layers of neurons. Each neuron receives inputs, applies an activation function to those inputs, and then passes the output to the next layer. This process continues until the final layer produces the desired output.\n\nNeural networks learn by adjusting the strength of connections between neurons, known as weights, based on the input data. This training process helps the network improve its accuracy and make predictions on new, unseen data.\n\nThere are different types of neural networks, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and others. Each type is suited for different types of data and tasks."
}
]
},
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"type": "string",
"value": "Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain. They consist of interconnected nodes, called neurons, that process and transmit information. Neural networks are used for tasks such as pattern recognition, classification, and regression.\n\nIn a neural network, information is passed through multiple layers of neurons. Each neuron receives inputs, applies an activation function to those inputs, and then passes the output to the next layer. This process continues until the final layer produces the desired output.\n\nNeural networks learn by adjusting the strength of connections between neurons, known as weights, based on the input data. This training process helps the network improve its accuracy and make predictions on new, unseen data.\n\nThere are different types of neural networks, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and others. Each type is suited for different types of data and tasks."
},
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