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8:49
YouTube
Super Data Science
Deep Learning Optimization: Stochastic Gradient Descent Explained
In this video, we explore the key differences between Gradient Descent and Stochastic Gradient Descent (SGD) *Course Link HERE:* https://community.superdatascience.com/c/dl-az Gradient Descent and Stochastic Gradient Descent (SGD) are the two essential methods for optimizing neural networks in deep learning. Learn how SGD speeds up optimization ...
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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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