Stochastic Gradient Descent is an iterative optimization algorithm used to train machine learning models by minimizing a cost function. Instead of computing the gradient using the entire dataset, it approximates the gradient using a single randomly selected data point or a small batch of data. This approach makes the training process faster and more memory-efficient, especially for large datasets. It adjusts model parameters incrementally.
Context
Stochastic Gradient Descent is a fundamental algorithm in the development and training of artificial intelligence models that can be applied to various aspects of digital assets. These applications include predicting market trends, optimizing trading strategies, and detecting fraudulent activities on blockchain networks. Research explores how to efficiently execute such computations in decentralized and privacy-preserving 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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