An intuition-first exploration of calculus using code and visualization, aimed at understanding the mathematics of change and its role in machine learning.
This repository contains a collection of Jupyter notebooks where I explore core calculus concepts through:
- Visualizations
- Numerical experiments
- Simple implementations in Python
The goal is to build strong intuition, not just memorize formulas.
Calculus can feel abstract. Instead of focusing only on theory, this repo emphasizes:
- Seeing how functions behave
- Understanding derivatives visually
- Connecting math to real-world intuition (and eventually machine learning)
calculus-essentials/
│
├── 01_derivatives/
│ ├── plot_derivative.ipynb
│ ├── plots.ipynb
│
├── 02_/ # coming soon
├── 03_/ # coming soon