Progressively curated · Open source

AI ROADMAP
& KNOWLEDGE BASE

//MATH FOUNDATIONS

One repo, one path: from calculus to autonomous agents. Every topic documented, linked, and updated as the field moves.

// learning-path.sys

THE ROADMAP

SIX STAGES · SEQUENTIAL PROGRESSION · SCROLL TO TRACE THE PATH

  1. 01planned

    stage-01.math

    MATH

    Foundations

    The language everything else is written in — build it once, use it forever.

    • Linear Algebra
    • Calculus
    • Probability & Statistics
    • Optimization
    ENTER MODULE
  2. 02planned

    stage-02.ml

    ML FUNDAMENTALS

    Core Concepts

    Supervised and unsupervised learning, evaluation, and the classic algorithms.

    • Regression & Classification
    • Feature Engineering
    • Model Evaluation
    • Classical Algorithms
    ENTER MODULE
  3. 03planned

    stage-03.dl

    DEEP LEARNING

    Neural Systems

    Neural networks from first principles through CNNs, RNNs, and transformers.

    • Neural Networks
    • CNNs & RNNs
    • Transformers
    • PyTorch / TensorFlow
    ENTER MODULE
  4. 04planned

    stage-04.genai

    GENERATIVE AI

    LLMs & Beyond

    Large language models, prompting, retrieval, and fine-tuning in practice.

    • LLMs
    • Prompt Engineering
    • RAG
    • Fine-tuning
    ENTER MODULE
  5. 05planned

    stage-05.agents

    AGENT ENGINEERING

    Autonomous Systems

    Tool use, orchestration, memory, and multi-agent systems that act on their own.

    • Tool Calling
    • MCP
    • Multi-Agent Systems
    • Orchestration Frameworks
    ENTER MODULE
  6. 06planned

    stage-06.capstone

    CAPSTONE PROJECTS

    Applied Builds

    End-to-end projects that pull every prior stage into one shipped system.

    • End-to-End Builds
    • Production Deployment
    • Portfolio Projects
    ENTER MODULE

// changelog.log

LAST UPDATED

2026-07-27

  1. 2026-07-27INITRepository scaffolded — roadmap structure and homepage design established.