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Computation Graph

A directed acyclic graph that records every operation in a computation, enabling automatic gradient computation via backpropagation.

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Understanding Micrograd Build a neural network from scratch — Karpathy's autograd engine, neurons, layers, and training — in pure Python with zero dependencies.
Understanding microGPT Walk through Karpathy's 200-line GPT implementation — from autograd and tokenization to multi-head attention, training, and text generation.
Understanding Makemore Build character-level language models from scratch — from bigram counting through MLPs, batch normalization, and WaveNet — generating new names with PyTorch.

lessons

Manual Backpropagation Understanding Makemore / Becoming a Backprop Ninja
Backprop Through Layers Understanding Makemore / Becoming a Backprop Ninja
The Full Backward Pass Understanding Makemore / Becoming a Backprop Ninja
The Value Class Understanding microGPT / Automatic Differentiation
Backpropagation Understanding microGPT / Automatic Differentiation
Embeddings: Tokens and Positions Understanding microGPT / The Transformer Architecture
RMSNorm: Stabilizing Activations Understanding microGPT / The Transformer Architecture
Loss and Next-Token Prediction Understanding microGPT / Training
The Training Loop Understanding microGPT / Training
What Is Micrograd? Understanding Micrograd / The Big Picture
Wrapping Scalars Understanding Micrograd / The Value Class
The Neuron Understanding Micrograd / Building a Neural Network
From Micrograd to PyTorch Understanding Micrograd / The Full Picture

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