Bridging the Gap Between Theory and Practice in Machine Learning
1:00:23
Bridging the Gap Between Theory and Practice in Machine Learning
Feb 8, 2018
Microsoft
In deep learning, why and how does SGD find global minima for non-convex and non-smooth neural networks? Microsoft researchers resolved to shed some light on this mystery: https://aka.ms/AA3ftja | Microsoft Research
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Insightforge | AI & Data Science on Instagram: "Stochastic Gradient Descent (SGD) is an optimization technique used to reduce a model’s loss and improve its performance. A good way to imagine it is like walking down a hill to reach the lowest point. The gradient tells you the slope and the direction to move downward. But instead of looking at the entire landscape (the full dataset), SGD looks at small random batches of data to estimate the slope at each step. Because it works with smaller sample
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