SENATOROVAI/multy-linear-regression-gradient-descent-stepik: v0.1.0
Abstract
Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters iteratively using small, random subsets (batches) of data, rather than the entire dataset. It significantly speeds up training for large datasets, though it introduces noise that causes, in some cases, heavy fluctuations.deep learning/neural networks.solver
Fields of Study & Subjects
Stochastic Gradient Optimization Techniques
Machine Learning and Data Classification
Neural Networks and Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 1 March 2026
- Language
- EN