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

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Emerging

This project helps you understand and implement the core optimization techniques—Gradient Descent and Stochastic Gradient Descent—that power machine learning and deep learning models. It takes your raw data and a loss function, guiding you through how these algorithms iteratively adjust model parameters to find the best fit. Aspiring machine learning engineers, data scientists, or AI researchers who want to deeply grasp the 'how' behind training models will find this useful.

Use this if you need to build a fundamental understanding of how machine learning models are optimized and want to implement these algorithms from scratch.

Not ideal if you are looking for a high-level library to quickly train pre-built machine learning models without delving into the underlying optimization mathematics.

machine-learning-engineering deep-learning-fundamentals data-science-optimization algorithm-implementation neural-network-training
No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 15 / 25
Community 18 / 25

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Stars

17

Forks

14

Language

Jupyter Notebook

License

MIT

Last pushed

Mar 05, 2026

Commits (30d)

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