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

Summary

In this technical lecture, Andrej Karpathy explains the fundamental mechanics of training neural networks by building 'micrograd', a minimal, scalar-valued autograd engine. He demystifies backpropagation as a recursive application of the chain rule through an expression graph. By implementing the library step-by-step in Python, starting from basic arithmetic and activation functions like tanh, he shows that modern deep learning libraries like PyTorch are essentially highly optimized versions of these same fundamental principles.

Chapters

1. 0:00Introduction to Micrograd
2. 8:00Understanding Derivatives and Calculus
3. 32:00Building the Value Data Structure
4. 56:00Visualizing Expressions and Graphs
5. 1:16:00Manual Backpropagation and Chain Rule
6. 1:37:00Neural Net Implementation
7. 1:58:00Training the Neural Network
8. 2:17:00Advanced Concepts and PyTorch

Topics covered

• Backpropagation Mechanics
• Scalar-Valued Autograd Engine

+4 more topics (full content when you follow the channel)

Transcript excerpt

hello my name is andre and i've been training deep neural networks for a bit more than a decade and in this lecture i'd like to show you what neural network training looks like under the hood so in particular we are going to start with a blank jupiter notebook and by the end of this lecture we will define and train in neural net and you'll get to see everything that goes on under the hood and exactly sort of how that works on an intuitive level now specifically what i would like to do is i would like to take you through building of micrograd now micrograd is this library that i released on github about two years ago but at the time i only uploaded the source code and you'd have to go in by yourself and really figure out how it works so in this lecture i will take you through it step by step and kind of comment on all the pieces of it so what is micrograd and why is it interesting good um…