🔢 Neural Network Basics

To understand how neural networks utilize Backpropagation and Reinforcement Learning, you must master the underlying mathematical engines.

Linear Algebra

Matrix Calculations & Tensors

Data in AI is represented as multidimensional arrays (Tensors). Matrix operations form the backbone of weight updates.

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Calculus

Differentiation & PID Control

The "Chain Rule" is the heart of backpropagation. Essential for **PID controllers** used in robotic self-balancing.

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Differential Equations

Dynamic Algorithmic Modelling

Critical for systems that change over time, helping autonomous agents maintain stability.

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