iiitl / iiitl/Logistic-Regression
Implement sigmoid and BCE with numerical sanity checks
Open
medium
scratch
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 16
- PR merge metrics
- No merged PRs in 30d
Description
Implement sigmoid and binary cross-entropy from scratch and validate outputs on a toy example.
Confirm shape correctness and finite loss values before full training.
Contributor guide
Research direction
Start in the repository's Jupyter Notebook and locate the area where the logistic-regression functions are implemented. Add sigmoid and binary cross-entropy from scratch, then validate them on a toy example with shape checks and finite loss values before full training.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- machine-learning, testing
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 52/100