π₯ LeetCode for PyTorch β practice implementing softmax, attention, GPT-2 and more from scratch with instant auto-grading. Jupyter-based, self-hosted or try online.
PyTorch implementations of modern open-source LLM architectures (Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and more) β written from scratch for readability and learning, based on Sebastian Raschka's LLM Architecture Gallery.
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.