How does a neural network actually learn to be less wrong? Not the hand-wavy version. The real one....
A GoPenAI series covers neural network math — MSE, gradient descent, and backpropagation — from first principles with no steps skipped. Engineers who understand backprop debug training failures faster and make better calls on architecture and optimization.
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
The Math Behind Neural Networks — Explained Like Nobody Did for Me 🧨 ...
The paper adds to ongoing work in the field aimed at demystifying deep learning, suggesting that what once appeared to be…
An interactive guide to the neural network that learned to read handwritten digits. Draw a digit and...
A no-jargon, story-first walkthrough of linear regression using a side project I actually ran on myself in Bengaluru. Math by…
Pulling back the curtain on modern machine learning architecture reveals something entirely...
Author(s): Kamrun Nahar Originally published on Towards AI. SARIMAX, Prophet, XGBoost, LSTM, and N-BEATS broken down without any…
