motasemwed/optimization-algorithms-comparison

A practical comparison of classical optimization algorithms (GD, SGD, Momentum, Adam, RMSProp, Adagrad, Newton) analyzing convergence speed, stability, and loss minimization for machine learning.

11
/ 100
Experimental
No License No Package No Dependents
Maintenance 10 / 25
Adoption 0 / 25
Maturity 1 / 25
Community 0 / 25

How are scores calculated?

Stars

—

Forks

—

Language

Jupyter Notebook

License

—

Category

gradient-descent-optimizers

Last pushed

Jan 29, 2026

Commits (30d)

0

GitHub
Gradient Descent Optimizers · 67 frameworks

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/motasemwed/optimization-algorithms-comparison"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.

Higher-rated alternatives

metaopt/torchopt

TorchOpt is an efficient library for differentiable optimization built upon PyTorch.

68

SimplexLab/TorchJD

Library for Jacobian descent with PyTorch. It enables the optimization of neural networks with...

65

clovaai/AdamP

AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights (ICLR 2021)

62

nschaetti/EchoTorch

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on...

60

opthub-org/pytorch-bsf

PyTorch implementation of Bezier simplex fitting

58

Explore ML Frameworks

All categories Trending ML Framework directory Insights