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Stochastic Gradient Descent in Deep Learning by poojamani133 is a document available to read on EtoBox.

Stochastic Gradient Descent (SGD) is a key algorithm in Deep Learning that optimizes objective functions by using a single sample or mini-batch at each step, making it suitable for large datasets. Unlike traditional Gradient Descent, which can be computationally intensive, SGD reduces the number of calculations significantly, allowing for efficient handling of big data and quick updates when new data is available. The document outlines the basic principles of SGD, its advantages over standard Gradient Desce

Author

poojamani133

Language

EN