yunjey/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
Covers fundamental to advanced architectures—from linear regression and CNNs through ResNets and RNNs, to GANs and variational autoencoders—with minimal, self-contained implementations (typically under 30 lines each). Structured progressively across basics, intermediate, and advanced modules, each with runnable examples and TensorBoard integration for visualization. Designed as a bridge between PyTorch's official quickstart and production research code.
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