Micrograd tutorial: backpropagation from scratch

I recommend watching Andrej Karpathy's recent videos on backprop and neural nets. Makes it really simple and intuitive. The spelled-out intro to neural networks and backpropagation: [VIDEO] Becoming a backprop ninja: [LINK]

For those who dont know, Andrej has a brilliant series of videos on youtube where he explores the basics of neural nets right up to transformer architecture through code. Starts here [0] and most recent is building GPT from scratch in code. [VIDEO]

The ChatGPT video by Karpathy is the last in a 7 video series. The one that really hit it home for me was the first video on MicroGrad [1]. The second video builds on MicroGrad and is also great for understanding how a basic NN works. [1] [VIDEO]

I highly recommend the video from karpathy [VIDEO] - This explains the same idea spelled out in code. It really helped me understand the mathematical expression graph of neural networks. He uses graphviz to show the expressions and initially calculate gradients manually and then implement back propagation.

Andrej Karpathy's "zero to hero" series [1] was how I learned the fundamentals of this stuff. It's especially useful because he explains the why and provides intuitive explanations instead of just talking about the what and how. Would recommend it if you haven't checked it out already. [1] [VIDEO]

Watch Karpathy's recent lectures. They're gold. Start here[1] with micrograd[2]. It doesn't use linear algebra/matrices to start, but the principles are the same. The matrix multiplication is how the weights of the connections between neurons and the input values are combined (to form an activation value that then may lead to that neuron "firing" or not, depending on whether it passes some threshold function). We use matrices to model the connections between neurons - each row is a connection, and each column is a weight corresponding to an input. [1] [VIDEO] [2] [LINK]

In the same vein, Karpathy's video series "Neural Networks from zero to hero"[0] touches upon a lot of this and intuitions as well. One of the best introductory series (even if you ignore the neural net part of it) and brushes on gradients, differentiation and what it means intuitively. [0] [VIDEO]

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]