The Math Behind Neural Networks — Explained Like Nobody Did for Me 🧨
The article discusses the mathematical foundations of neural networks, aiming to provide a clear understanding of how they learn. It emphasizes the importance of concepts like derivatives, gradient descent, and backpropagation without oversimplifying the explanations. The author shares a series of articles designed to fill gaps often left by other tutorials.
- ▪The series begins with linear regression and error measurement.
- ▪It explains how neural networks learn to minimize errors through mathematical concepts.
- ▪The author aims to demystify complex topics like backpropagation and gradient descent.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1277859) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Hussein Mahdi Posted on May 24 The Math Behind Neural Networks — Explained Like Nobody Did for Me 🧨 #datascience #machinelearning #deeplearning #ai 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
