Back to Basics
Understand the foundations behind AI and computing.
Why Back to Basics?
AI tools and frameworks are changing rapidly. The underlying ideas are much more stable.
This section collects material that helps build a solid understanding of the foundations behind AI and intelligent computing.
The emphasis is on understanding rather than memorizing, and on going back to first principles when it helps explain how something actually works.
Maths for AI
▶ Vectors: The Language of Modern AI
▶ Tensors: Extending Vectors and Matrices
▶ Beyond 3D: Extending the Mathematics We Already Know
▶ Optimization: How Machines Find Better Solutions
▶ Conditional Probabilty & Bays' Theorem (Series)
Linear Programming- A Practitionar's Guide
▶ Linear Programming Part 0- About This Series
▶ Linear Programming Part 1- Where LP Fits in AI Systems
▶ Linear Programming Part 2- Fundamental Building Blocks
▶ Linear Programming Part 3- Solving LP Problems
▶ Linear Programming Part 4- Practical Scenarios
▶ Linear Programming Part 5- LP Algorithms
▶ Linear Programming Part 6- LP vs Integer Programming vs Mixed-Integer Programming
▶ Linear Programming Part 7- Limiations of LP
▶ Linear Programming Part 8- Sensitivity Analysis
▶ Linear Programming Part 9- Understanding Solver Output
▶ Linear Programming Part 10- End to End Case Study