SENATOROVAI/stochastic-average-gradient-sag-saga-solver-course
The SAG (Stochastic Average Gradient) + SAGA (Accelerated) solver is an optimization algorithm used primarily in machine learning, specifically for logistic regression and linear support vector machines (SVMs) within libraries like scikit-learn. It is designed to be highly efficient for large datasets with many samples and features. Solver
Stars
19
Forks
14
Language
Python
License
MIT
Category
Last pushed
Mar 01, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SENATOROVAI/stochastic-average-gradient-sag-saga-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