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The spelled-out intro to neural networks and backpropagation: building micrograd

Andrej KarpathyAI-ML
17chapters with key takeaways — read first, then watch
1

Introduction to Micrograd and Backpropagation

0:00-6:44•6m 44sIntro
2

Intuition Behind Derivatives and Local Gradients

6:44-19:05•12m 21sConcept
3

Implementing the `Value` Object and Graph Visualization

19:05-29:32•10m 27sArchitecture
4

Manual Backpropagation: Plus and Multiply Nodes

29:32-51:11•21m 39sConcept
5

Gradient Descent Intuition and Neuron Model

51:11-1:01:30•10m 19sConcept
6

Manual Backpropagation Through Tanh Activation

1:01:30-1:08:35•7m 5sConcept
7

Automating the Backward Pass with Topological Sort

1:08:35-1:22:27•13m 52sArchitecture
8

Critical Bug Fix: Gradient Accumulation

1:22:27-1:27:05•4m 38sLimitation
9

Extending `Value` Object with More Operations

1:27:05-1:39:28•12m 23sArchitecture
10

Micrograd vs. PyTorch Autograd API

1:39:28-1:43:56•4m 28sArchitecture
11

Building Neural Network Modules: Neuron, Layer, MLP

1:43:56-1:50:28•6m 32sArchitecture
12

Training an MLP: Loss Function and Parameters

1:50:28-1:57:56•7m 28sTraining
13

Implementing the Gradient Descent Training Loop

1:57:56-2:10:20•12m 24sTraining
14

The Critical 'Zero Grad' Bug and Its Solution

2:10:20-2:14:03•3m 43sLimitation
15

Comprehensive Summary of Neural Network Training

2:14:03-2:16:44•2m 41sConcept
16

Micrograd Codebase and PyTorch Backend Insights

2:16:44-2:25:22•8m 38sArchitecture
17

Conclusion: Understanding Neural Nets Under the Hood

2:25:22-2:25:52•30sConclusion

Video Details & AI Summary

Published Aug 16, 2022
Analyzed Dec 8, 2025

AI Analysis Summary

This lecture provides a comprehensive, intuitive, and hands-on introduction to neural networks and backpropagation by building a simplified autograd engine called micrograd. It covers the mathematical intuition of derivatives and the chain rule, implements a `Value` object for tracking computational graphs, and demonstrates manual and automated backpropagation. The video culminates in building a multi-layer perceptron and training it using gradient descent, highlighting critical concepts like gradient accumulation and the 'zero grad' bug, while also comparing micrograd's API to PyTorch.

Title Accuracy Score
10/10Excellent
53.9s processing
Model:gemini-2.5-flash

Original Video

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