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

This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school. Links: - micrograd on github: https://github.com/karpathy/micrograd - jupyter notebooks I built in this video: https://github.com/karpathy/nn-zero-to-hero/tree/master/lectures/micrograd - my website: https://karpathy.ai - my twitter: https://twitter.com/karpathy - "discussion forum": nvm, use youtube comments below for now :) - (new) Neural Networks: Zero to Hero series Discord channel: https://discord.gg/3zy8kqD9Cp , for people who'd like to chat more and go beyond youtube comments Exercises: you should now be able to complete the following google collab, good luck!: https://colab.research.google.com/drive/1FPTx1RXtBfc4MaTkf7viZZD4U2F9gtKN?usp=sharing Chapters: 00:00:00 intro 00:00:25 micrograd overview 00:08:08 derivative of a simple function with one input 00:14:12 derivative of a fu