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What You Need Before Neural Networks

Master these foundational concepts before diving into neural networks. Each prerequisite builds toward understanding how neural networks learn.

4-6 weeks to prepare

10 topics to cover

5 essential

You cannot understand neural networks without these

5 topics

matrices

Neural networks are matrix operations - weights, activations, and layers are all matrices

vectors basics

Input data, weights, and gradients are represented as vectors

gradient descent

The core algorithm for training neural networks

chain rule

Backpropagation IS the chain rule applied repeatedly

partial derivatives

Computing gradients requires partial derivatives

Strongly recommended for deeper understanding

3 topics

probability basics

Loss functions and outputs are often probabilistic

convexity

Understand why neural network optimization is hard (non-convex)

eigenvalues

Understanding stability, PCA layers, and network dynamics

Nice to have but not strictly required

2 topics

linear

Linear regression is a single-layer neural network

logistic

Logistic regression is a neural network with sigmoid activation

Once you've covered the essential prerequisites, you'll have a solid foundation for understanding neural networks.