Machine learning training is the process of feeding data to an algorithm, allowing it to learn patterns and relationships without explicit programming. During this phase, the model adjusts its internal parameters based on the input data and a defined objective function. The goal is to optimize the model’s performance for specific tasks, such as prediction or classification. This iterative process refines the model’s ability to generalize.
Context
Machine learning training is increasingly relevant in the digital asset space for tasks like market sentiment analysis, anomaly detection in transaction flows, and risk assessment for decentralized finance applications. Concerns often center on data privacy, the potential for bias in training data, and the computational resources required. Advancements in privacy-preserving machine learning techniques are particularly pertinent to blockchain environments.
A novel Proof-of-Learning mechanism replaces Byzantine security with incentive-security, provably aligning rational agents to build a decentralized AI compute market.
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