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Khanz9664 / repository
This GitHub repository contains a personal portfolio project. It is built using HTML, CSS, and JavaScript. The primary purpose of this repository is to showcase the my skills and projects through a visually engaging website.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
Experience the high-fidelity design system built with custom modular CSS and cinematic GSAP animations.
The portfolio is architected as a modular, high-performance static environment, prioritizing cinematic visuals without compromising on core DevOps stability.
graph TD
subgraph "Core Engine Layer"
GSAP["GSAP & ScrollTrigger<br/>(Animation Control)"]
CanvasJS["Generative Backgrounds<br/>(Math-driven Canvas)"]
MathJax["MathJax Rendering<br/>(Scientific Articles)"]
end
subgraph "Structural Layer"
HTML5["Semantic HTML5<br/>Modular UI Templates"]
CSS3["Modular CSS System<br/>(Zero-Error Standard)"]
end
subgraph "DevOps & Quality Gates"
GHA["GitHub Actions CI/CD"]
Lint["Stylelint & htmlhint<br/>(Static Analysis)"]
Tests["Playwright E2E Tests<br/>(Functional Verification)"]
end
HTML5 --> GSAP
HTML5 --> MathJax
CSS3 --> HTML5
CanvasJS --> GSAP
GHA --> Lint
GHA --> Tests
Lint --> |Passes| GHA
Tests --> |Passes| GHA
GHA --> |Atomic Deploy| GH_Pages["Production Env (live)"]
style GSAP fill:#bd00ff,stroke:#333,stroke-width:2px,color:#fff
style GHA fill:#2ea44f,stroke:#333,stroke-width:2px,color:#fff
style Tests fill:#00f2ff,stroke:#333,stroke-width:2px,color:#111
Selected from shared topics, language and repository description—not editorial ratings.
roadmap-sh-kzwolenik95 /
This repository offers a GitHub Actions workflow example to automatically deploy a static website to GitHub Pages when index.html changes in the main branch, demonstrating CI/CD basics.
33/100 healthumarcode01 /
This repository showcases a web application that has been seamlessly deployed using a Continuous Integration and Continuous Deployment (CI/CD) pipeline with Vercel and GitHub Actions.
34/100 healthEvery commitment to the production environment undergoes a rigorous evaluation through our automated quality gates.
sequenceDiagram
participant Dev as Developer
participant GHA as GitHub Actions CI
participant QG as Quality Gates (Lint/Test)
participant Prod as GitHub Pages
Dev->>GHA: Git Push (Main)
activate GHA
par Quality Audit
GHA->>QG: Execute htmlhint & stylelint
GHA->>QG: Execute Playwright E2E Suite
end
QG-->>GHA: Success Report
rect rgb(20, 40, 20)
Note right of GHA: Deployment Gate
GHA->>Prod: Trigger Atomic Deployment
Prod->>Prod: Build & Sync
Prod->>Dev: Deployment Live
end
deactivate GHA
To guarantee a premium user experience across all devices, we employ a Playwright-driven testing suite.
375px, 768px, 1024px).A core pillar of this portfolio is the "Machine Learning From First Principles" series. These are rigorous mathematical derivations rendered with MathJax.
| Category | Tools | Standard |
|---|---|---|
| Animation | GSAP 3.12+ | Cinematic / Scroll-synced |
| Logic | Vanilla JS (ES6+) | Modular / Modern |
| Quality | Playwright, Stylelint | Zero-Error Policy |
| Hosting | GitHub Pages | Atomic CI/CD |
git clone https://github.com/Khanz9664/portfolio.git
npm install
npm test # Run headless suite
npm run test:ui # Open Playwright UI
npx stylelint "**/*.css"
| Article | Description | Category |
|---|---|---|
| Gradient Descent | The workhorse of machine learning optimization. Understand partial derivatives, learning rates, and convergence behavior from first principles. | Optimization |
| Lagrange Multipliers | Constrained optimization unlocked. A deep dive into the method of Lagrange multipliers, dual problems, and their geometric intuition. | Optimization |
| Bias Variance TradeOff | The fundamental trade-off between model simplicity and prediction accuracy. | Optimization |
| Linear Regression | The foundation of predictive modeling. Complete mathematical derivation of Ordinary Least Squares, normal equations, and assumptions. | Supervised Learning |
| Logistic Regression | Moving from continuous to categorical. Explore sigmoid functions, maximum likelihood estimation, and cross-entropy loss gradients. | Classification |
| Neural Networks | The mathematical foundations of deep learning. Explore forward propagation, backpropagation derivations, and the universal approximation theorem. | Deep Learning |
| Principal Component Analysis | From variance maximization to SVD equivalence. A definitive guide to understanding PCA's mathematical machinery from the ground up. | Dimensionality Reduction |
| Information Theory | The mathematics of uncertainty and learning. Explore Shannon entropy, KL divergence, cross-entropy, and the maximum entropy principle. | Information Theory |
| Singular Value Decomposition | The most powerful factorization in all of mathematics. Works on every matrix, reveals hidden geometry, and underlies PCA and compression. | Linear Algebra |