Describe your change:

• Add an algorithm
• Fix a bug or typo in an existing algorithm
• Add or change doctests
• Documentation change

This PR adds the Stochastic Gradient Descent (SGD) optimizer under
machine_learning/neural_network/optimizers/sgd.py. SGD is a fundamental optimizer used for training neural networks and deep learning models.

Key features included in this PR:

• Clear, educational implementation suitable for learners
• Type hints for all parameters and return values
• Doctest included to demonstrate usage
• Unit test in test_sgd.py to validate functionality

This PR is the first step in adding a sequence of neural network optimizers:
Momentum SGD, Nesterov Accelerated Gradient (NAG), Adagrad, Adam, and Muon.

This implementation provides a reference-quality example and lays the foundation for future contributions.

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.

Fixes #13662