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

What people said

jwitthuhn · 2025-12 · Strong recommendation

If you have 20 hours to spare I highly recommend this youtube playlist from Andrej Karpathy [VIDEO] It starts with the fundamentals of how backpropagation works then advances to building a few simple models and ends with building a GPT-2 clone. It won't taech you everything about AI models but it gives you a solid foundation for branching out.

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omrishn · 2025-11 · Strong recommendation

This blog post analyzes the code with an emphasis on the small details and ideas explored in Andrej Karpathy's awesome micrograd lecture. Link to the original lecture: [VIDEO] I'd love to hear any thougts/reviews about the blog post.

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jackdoe · 2025-06 · Strong recommendation

For studying? Mainly watching and re-watching Karpathy's 'Zero To Hero'[1] and Stanford's 'Introduction to Convolutional Neural Networks for Visual Recognition'[2], also a lot of transformers from scratch videos like Umar Jamali's videos[3], and I also study backwards to McCulloch and Pitts. Reading the 30 papers [LINK] and so on. And of course Yannic Kilcher[4], and also listening in on the paper discussions they do on discord. Practicing a lot with just doing backpropagation by hand and making toy models by hand to get intuition for the signal flow, and building all kinds of smallish systems, e.g. how far can you push whisper, small qwen3, and kokoro to control your computer with voice? People think that deepseek/mistral/meta etc are democratizing AI, but its actually Karpathy who teaches us :) so we can understand them and make our own. [1] [VIDEO] [2] [LINK] [3] [LINK] [4] [LINK]

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