Akshaya Parida - Portfolio

Welcome to my portfolio. This is a comprehensive showcase of my skills, projects, and expertise in AI Engineering, with a focus on the mathematical foundations that power modern AI systems.

Overview

This portfolio serves as both a professional showcase and an educational resource. It demonstrates my expertise in:

• AI Engineering: Implementation of AI systems and algorithms
• Mathematical Foundations: Deep understanding of the mathematics behind AI
• Full-Stack Development: Modern web technologies and deployment
• Open Source Contributions: Quality code and documentation

What This Portfolio Provides

Educational Content

• Mathematics for AI Engineers: Comprehensive guide covering linear algebra, calculus, probability & statistics, and linear models
• Interactive Visualizations: Real-time mathematical concepts visualization
• Theoretical Explanations: Deep dives into how mathematical concepts apply to AI
• Practical Code Examples: Real-world implementations with detailed explanations

Technical Showcase

• Modern Web Technologies: Built with Next.js 15, TypeScript, and React
• Performance Optimized: Code splitting, dynamic imports, and optimized loading
• Responsive Design: Works seamlessly across all device sizes

Professional Insights

• Project Demonstrations: Showcases of my work and technical capabilities
• Learning Journey: Documented path of continuous learning and skill development
• Technical Blog: Insights and articles on technology and development

Key Features

Mathematics Learning Platform

• Interactive visualization tools for mathematical concepts
• Gradient descent playground
• Vector space demonstrations
• Matrix multiplication visualizations
• PCA and other statistical method examples

Performance Optimizations

• Code splitting for fast initial loads
• Dynamic imports for on-demand content loading
• Automated testing in CI pipeline

Modern Development Practices

• TypeScript for type safety
• Jest for testing
• ESLint for code quality
• Husky for Git hooks
• Semantic versioning

Learning Resources

This portfolio provides educational content that helps users understand:

• Linear Algebra: Vectors, matrices, and transformations - the language of neural networks
• Calculus: Derivatives and optimization - how neural networks learn
• Probability & Statistics: Uncertainty and inference - foundation of machine learning
• Linear Models: From linear regression to neural networks - the building blocks

MLOps Learning Platform

• Data Exploration: EDA, visualization, and dataset understanding
• Data Validation: Schema validation with Pandera, preprocessing pipelines
• Reproducible Training: MLflow experiment tracking, DVC pipelines, LightGBM
• Model Deployment: FastAPI serving, Pydantic validation, Docker containerization

Technical Stack

• Framework: Next.js 15 (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• Testing: Jest, React Testing Library
• Deployment: Vercel
• Package Manager: pnpm
• CI/CD: GitHub Actions

Built using Next.js, TypeScript, and React.