The video loads from YouTube only after you press Play.

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

A 2.4-hour lecture by Andrej Karpathy about autodifferentiation, backpropagation and neural networks, on the AI shelf. It was posted to r/MachineLearning as “[P] The spelled-out intro to neural networks and backpropagation: building micrograd (Andrej Karpathy 2h25m lecture)”. It is pitched at advanced level.

Andrej Karpathy, Lecture, 2 h 26 min, 2022, on the AI shelf

Karpathy builds micrograd to explain automatic differentiation and neural-network training.

Study notes

Advanced, Built from scratch. Step 7 of 11 in From probability to modern AI

Helpful first

• Python
• Derivatives
• Basic neural networks

What it builds

• Build an automatic-gradient engine.
• Represent calculations as an expression graph.
• Apply the chain rule backwards through the graph.

Useful moments

• 0:48Micrograd and automatic gradients
• 1:22An example expression
• 3:19Backpropagation with the chain rule

Check yourself: How can a global derivative be assembled from local derivatives in a computation graph?

Next in the path: Transformers, the tech behind LLMs | Deep Learning Chapter 5

Notes are based on metadata, Reddit context and selected English caption passages. They are not a review of every claim in the video.