pytorch-deep-learning and pytorch-tutorial
These resources are competitors, as both are standalone tutorials—one a course, the other a GitHub repository—teaching PyTorch for deep learning.
About pytorch-deep-learning
mrdbourke/pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Covers fundamental tensor operations, neural network architectures, computer vision, custom dataset loading, and modular code organization through hands-on notebooks and exercises. The curriculum progresses from PyTorch basics through classification and transfer learning, then advances to practical applications including experiment tracking, research paper replication, and model deployment. PyTorch 2.0 compatible materials are supplemented with video lectures, slides, and a reference cheatsheet.
About pytorch-tutorial
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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