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 β and I am sharing it here on dev.to for the first time.
Part 1 β Where the math actually begins
Slope β linear regression β 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?
π https://blog.gopenai.com/the-math-behind-neural-networks-explained-like-nobody-did-for-me-cda519ef63e8
Part 2 β How the network actually learns
- The derivative β slope, but instantaneous
- Gradient descent β walking down the loss surface
- Multiple layers β how a single neuron becomes a network
- Backpropagation β the chain rule, finally demystified π 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 β it might become a future chapter.
