SENATOROVAI/gradient-descent-sgd-solver-course
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
Stars
17
Forks
14
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 05, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SENATOROVAI/gradient-descent-sgd-solver-course"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
jolars/sortedl1
Python package for Sorted L-One Penalized Estimation (SLOPE)
gugarosa/opytimizer
🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
hiroyuki-kasai/SGDLibrary
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20
SENATOROVAI/L-BFGS-B-solver-course
Linear regression with the LBFGSB (Limited-memory Broyden-Fletcher-Goldfarb-Shanno BFGS) solver...
loeweX/Forward-Forward
Reimplementation of Geoffrey Hinton's Forward-Forward Algorithm