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Stochastic models in sklearn
In scikit-learn, stochastic models refer to a class of algorithms that involve randomness in their training process. These models are typically used for large-scale datasets when it is computationally expensive or infeasible to process the entire dataset at once. Instead, they perform updates on a subset of the data or use random sampling techniques.
Machine Learning – Part 4 – Gradient Based Models (Pre-Cursor To Deep Learning)
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Stochastic GD


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