[Feature Request]: Add Stochastic Gradient Descent model in Machine Learning

[pavitraag]

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Feature Description

Stochastic Gradient Descent (SGD) is a pivotal optimization algorithm in machine learning, iteratively updating model parameters based on gradients computed from mini-batches or individual data points. This method is crucial for efficiently handling large datasets and real-time learning scenarios. By adjusting model weights in the direction that reduces the loss function, scaled by a learning rate, SGD accelerates convergence and supports the training of complex models like neural networks. Careful selection of hyperparameters, such as the learning rate, is essential for optimizing SGD's performance and achieving effective model training across various machine learning applications.

Use Case

-Online Advertising and Recommendation Systems -Healthcare and Personalized Medicine

Priority

High

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[github-actions[bot]]

Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!

[github-actions[bot]]

Hello @pavitraag! Your issue #3189 has been closed. Thank you for your contribution!