Neural Network Math Explained in 90 Seconds
HarperSCarroll · x · 2026-07-14
This is an intro to a 90-second video explaining the foundational math behind neural networks.
- Breaks down neural networks into input, hidden, and output layers
- Every connection has a weight, and a neuron's value is essentially the “sum of inputs × weights”
- The example uses two inputs and two sets of weights to demonstrate forward calculation: first computing hidden layer neuron values, then passing them to the next layer
- The author notes that this demo omits the non-linear function, promising to cover those crucial parts and how weights are learned via backpropagation in future videos
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