EimanTahir071/Mathematics-for-Machine-Learning-and-Artificial-Intelligence
Mathematical Concepts - Linear algebra, statistics, calculus foundations for ML/AI
What's novel
Mathematical Concepts - Linear algebra, statistics, calculus foundations for ML/AI
Code Analysis
13 files read · 3 roundsA collection of ~20 small standalone Python scripts (10-40 lines each) that demonstrate individual mathematical concepts (matrix ops, SVD, symbolic derivatives, basic gradient descent, Bayes' theorem, t-tests) using NumPy, SymPy, and SciPy.
Strengths
The implementations are mathematically correct for what they demonstrate, and the SVD example (day2_ex3.py) is the most thorough piece with reconstruction verification and low-rank approximation. The mini-project linear regression from scratch is a reasonable, complete small exercise.
Weaknesses
The README claims coverage of PCA, Adam optimizer, KL divergence, Lagrange multipliers, backpropagation, and many other topics that do not exist anywhere in the code. There are zero tests, no package structure, no requirements.txt, and the code is entirely flat standalone scripts with no shared logic or reusable abstractions.
Score Breakdown
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Commits
107
Contributors
2
Files
33
Active weeks
2
Repository
Language
Python
Stars
2
Forks
0
License
MIT