The Math That Makes AI Work
Linear algebra, calculus, and probability through hands-on Python code. No proofs, just the intuition that matters.
Frameworks like PyTorch and TensorFlow hide a lot of math, but the math doesn't go away. It just becomes invisible until something breaks. Understanding the mathematical foundations1 lets you debug training failures, read research papers, and make informed architectural decisions. This tutorial covers the essential concepts with Python code for each one.
Tutorial Goals
- Linear Algebra — how data flows through AI models
- Calculus — how models learn and improve
- Probability & Statistics — how to handle uncertainty
- Every concept implemented in Python code
- Connections to real ML models you'll build next
Why Mathematics Matters
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Ana Clara MedeirosAI Developer
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What you get
6 modules and 42 hands-on lessons with runnable code, failure diagnosis, and measured comparisons
2 capstone repositories you build, test, evaluate, and keep as portfolio proof
Videos, source code, complete projects and architecture diagrams
Recordings of live sessions
Discord community access
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