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

Andrej Karpathy · Beginner ·📐 ML Fundamentals ·3y ago
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:…
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Chapters (19)

intro
0:25 micrograd overview
8:08 derivative of a simple function with one input
14:12 derivative of a function with multiple inputs
19:09 starting the core Value object of micrograd and its visualization
32:10 manual backpropagation example #1: simple expression
51:10 preview of a single optimization step
52:52 manual backpropagation example #2: a neuron
1:09:02 implementing the backward function for each operation
1:17:32 implementing the backward function for a whole expression graph
1:22:28 fixing a backprop bug when one node is used multiple times
1:27:05 breaking up a tanh, exercising with more operations
1:39:31 doing the same thing but in PyTorch: comparison
1:43:55 building out a neural net library (multi-layer perceptron) in micrograd
1:51:04 creating a tiny dataset, writing the loss function
1:57:56 collecting all of the parameters of the neural net
2:01:12 doing gradient descent optimization manually, training the network
2:14:03 summary of what we learned, how to go towards modern neural nets
2:16:46 walkthrough of the full code of micrograd on githu

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