A project-based curriculum that teaches PyTorch by rebuilding it from scratch. Start with scalar backpropagation, create a reverse-mode autodiff engine, expand it into tensors, then implement neural networks, optimizers, CNNs, attention, Transformers, ViTs, and a mini PyTorch framework. Every gradient is code you write and understand.
☆65Jul 11, 2026Updated 3 months ago
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