Basic Math Concepts – Deep Learning with Neural Networks
Math
| Topic | Why |
| Algebra | Weight updates, vector operations |
| Linear Equations | Forward pass through layers |
| Functions | Activation functions like sigmoid, ReLU |
| Derivatives | Backpropagation using chain rule |
| Matrix Multiplication | Layer connections in actual frameworks |
Statistics
| Topic | Why |
| Mean Squared Error (MSE) | Loss calculation |
| Gradient Descent | Optimization |
| Probability | Understanding uncertainty and sigmoid |
| Normalization | Data preprocessing (feature scaling) |
Deep Learning with Neural Networks – Summary