About — Neural Pathways

Why I Built This

Machine learning can feel like magic—until you see the numbers flow. Neural Pathways was born from a simple belief: understanding comes from seeing.

Too many ML resources throw equations at you without showing what they actually do. Here, every concept has an interactive visualization. Drag a point and watch the derivative change. Adjust weights and see the network respond. Play with matrices and observe transformations unfold.

This isn't about memorizing formulas—it's about building genuine intuition for how neural networks think.

Suggested Learning Path

1

Linear Algebra Foundations

Start with vectors, matrices, and transformations. These are the building blocks of everything.

2

Calculus Essentials

Learn derivatives, gradients, and the chain rule—the language of how networks learn.

3

Neural Network Operations

See forward passes and backpropagation in action with real numbers at every step.

4

Optimization & Playgrounds

Experiment with gradient descent, loss landscapes, and build your own networks.

Tech Stack

Built for performance and clarity. Every visualization runs smoothly so you can focus on learning.

Three.js Vanilla JS Tailwind CSS No frameworks

About the Author

PE

Peter Edwards

Software Developer passionate about making complex concepts accessible. Building tools that help people understand the math behind AI.

Inspiration & Credits

Standing on the shoulders of giants. This project draws inspiration from:

3Blue1Brown — For proving that math visualization changes everything
Distill.pub — For setting the standard in interactive ML explanations
Andrej Karpathy — For making neural networks approachable
The open-source community — For Three.js, Tailwind, and countless other tools