{"id":18317704,"url":"https://github.com/datarohit/learning-pytorch","last_synced_at":"2026-05-02T06:37:01.250Z","repository":{"id":183847688,"uuid":"670855493","full_name":"DataRohit/Learning-PyTorch","owner":"DataRohit","description":" Explore my journey of learning PyTorch through a series of hands-on projects! 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I used the following resource to learn PyTorch:\n\n- [Learn PyTorch](https://www.learnpytorch.io/)\n\nThe notebooks cover various topics and tasks related to PyTorch, including linear regression, multi-class classification, CNN, transfer learning, and more.\n\n## Notebooks\n\n1. `00_pytorch_fundamentals.ipynb` - An introduction to PyTorch fundamentals.\n2. `01_pytorch_workflow_linear_regression_v1.ipynb` - Linear regression using PyTorch (Version 1).\n3. `01_pytorch_workflow_linear_regression_v2.ipynb` - Linear regression using PyTorch (Version 2).\n4. `02_pytorch_multi_class_classification.ipynb` - Multi-class classification with PyTorch.\n5. `02_pytorch_multi_layer_binary_classification.ipynb` - Binary classification with multiple layers in PyTorch.\n6. `02_pytorch_multi_layer_non_linear_classification.ipynb` - Non-linear classification with multiple layers in PyTorch.\n7. `02_pytorch_multi_layer_regression.ipynb` - Multi-layer regression using PyTorch.\n8. `02_pytorch_non_linear_activation_functions.ipynb` - Exploring non-linear activation functions in PyTorch.\n9. `02_pytorch_non_linear_spiral_classification.ipynb` - Non-linear spiral classification with PyTorch.\n10. `02_pytorch_simple_binary_classification.ipynb` - Simple binary classification using PyTorch.\n11. `03_pytorch_fashion_mnist_cnn.ipynb` - Fashion MNIST classification using CNN in PyTorch.\n12. `03_pytorch_fashion_mnist_linear.ipynb` - Fashion MNIST classification using linear models in PyTorch.\n13. `03_pytorch_fashion_mnist_non_linear.ipynb` - Fashion MNIST classification using non-linear models in PyTorch.\n14. `03_pytorch_fashion_mnist_resnet50.ipynb` - Fashion MNIST classification using ResNet50 in PyTorch.\n15. `04_pytorch_custom_dataset.ipynb` - Working with custom datasets in PyTorch.\n16. `05_pytorch_transfer_learning.ipynb` - Transfer learning with PyTorch.\n17. `06_pytorch_experiment_tracking.ipynb` - Experiment tracking with PyTorch.\n18. `07_pytorch_vision_transformer.ipynb` - Vision Transformer (ViT) implementation in PyTorch.\n19. `08_pytorch_model_deployment.ipynb` - Model deployment using PyTorch.\n20. `09_pytorch_quick_pytorch_2.ipynb` - Quick introduction to PyTorch 2.0.\n\n## Learning Resource\n\nI used the following resource to learn PyTorch:\n\n- [Learn PyTorch](https://www.learnpytorch.io/)\n\n## Acknowledgments\n\nSpecial thanks to **Daniel Bourke** (Github username: mrdbourke) for providing valuable learning material and code examples. You can find his repository on PyTorch Deep Learning here: [PyTorch Deep Learning](https://github.com/mrdbourke/pytorch-deep-learning).\n\nFeel free to explore the notebooks and utilize the resources mentioned above to enhance your PyTorch skills. Happy learning!\n\n**Note**: If you plan to use any part of this repository or the models, please adhere to the respective licenses and terms of use.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatarohit%2Flearning-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatarohit%2Flearning-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatarohit%2Flearning-pytorch/lists"}