How does a neural network actually learn to be less wrong?
Not the hand-wavy version. The real one. The one with the derivative, the chain rule, and the loss surface that nobody draws for you when you start.
I got tired of tutorials that skip steps, so I wrote the series I wish I had when I began. No formula without explanation. No "as you can see." No magic.
It is now live on GoPenAI — and I am sharing it here on dev.to for the first time.
Part 1 — Where the math actually begins
Slope → linear regression → error (MSE). One continuous idea, built from the ground up. We stop right at the moment the real question appears: now that we can measure how wrong the model is, how does it learn to be less wrong?
👉 https://blog.gopenai.com/the-math-behind-neural-networks-explained-like-nobody-did-for-me-cda519ef63e8
Part 2 — How the network actually learns
- The derivative — slope, but instantaneous
- Gradient descent — walking down the loss surface
- Multiple layers — how a single neuron becomes a network
- Backpropagation — the chain rule, finally demystified 👉 https://blog.gopenai.com/the-math-behind-neural-networks-explained-like-nobody-did-for-me-7db69dc6f1bd
What is the one concept in neural network math that confused you the most when you started? Drop it in the comments — it might become a future chapter.
