micrograd

Train tiny neural networks and visualize backpropagation steps.

5|Updated Apr 15, 2026
One-click install
npx skills add https://github.com/47network/Sven --skill micrograd
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: micrograd
Source: https://github.com/47network/Sven/tree/main/skills/ai-agency/micrograd
Command: npx skills add https://github.com/47network/Sven --skill micrograd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Train tiny neural networks and visualize backpropagation steps.

Core Features & Use Cases

  • Value: scalar autograd node with full backpropagation
  • Neuron / Layer / MLP: build tiny neural networks from scratch
  • Training loop: SGD with MSE, hinge, and binary cross-entropy loss functions
  • Interactive walkthrough: 10-step guided tour from "what is a Value?" to "train a moon classifier"
  • Canvas visualization: decision boundary grids, parameter snapshots, and training curves for the Canvas UI

Quick Start

Start by creating a session, define a tiny network architecture, and run a short training to observe backpropagation in action.

Frequently Asked Questions about micrograd

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I visualize backpropagation in a neural network?

Yes, you can build neural networks from scratch by defining a tiny architecture using Neuron, Layer, and MLP components. A TypeScript handler and micrograd runtime manage the session and training workflows.

What loss functions can I use to train a tiny neural network?

You can train a tiny neural network using Stochastic Gradient Descent (SGD) with Mean Squared Error (MSE), hinge, or binary cross-entropy loss functions. These options support training across XOR, moon, and custom datasets.

Can I learn how gradient flow works without prior deep learning framework experience?

Yes, an interactive 10-step guided tour explains gradient flow from basic scalar autograd nodes to training a moon classifier. This visual walkthrough requires no prior deep learning framework experience.

What datasets are supported for demonstrating small-network training?

Small-network training is demonstrated using XOR, moon, and custom datasets. These datasets help visualize decision boundaries and parameter snapshots during the training loop.