About this document
Understanding Stochastic Gradient Descent by harshadvar9010 is a document available to read on EtoBox.
The document discusses the process of minimizing empirical risk to find optimal parameters for a model, using loss functions appropriate for regression and classification. It introduces stochastic gradient descent (SGD) as a computationally efficient method for gradient descent, utilizing unbiased estimators like mini-batches to approximate gradients. The document also mentions the importance of regularization to prevent overfitting and highlights the analysis of SGD variants through key descent lemmas.
- Author
- harshadvar9010
- Language
- EN