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pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side.
Features
- Progressive deep learning walkthroughs using PyTorch
- Build your first neural network with hands-on code
- Examples of regression, classification, and advanced models
- Reinforcement learning and GAN scripts included
- Readable code organized by topic
- Support for learning on CPU or GPU
Project Samples
