PyTorch Learning Journey 🔥

Just me learning PyTorch from scratch. Nothing fancy, just notebooks and experiments.

What's Inside

Zero to One/ - Main learning path with fundamentals, workflows, and classification stuff. Started with basics and working my way up.

Documentation/ - Official PyTorch tutorials and examples. FashionMNIST models and whatnot.

PyTorch Advanced Architectures and Deployment/ - Future stuff. Mostly empty for now but that's where I'm headed.

Structure

• Jupyter notebooks for everything
• Helper functions scattered around (helper_functions.py, helper_utils.py)
• Saved models in .pth files
• Some unit tests because why not

Topics Covered

• PyTorch fundamentals (tensors, operations, etc.)
• Basic workflow (data → model → training → inference)
• Neural network classification
• Model saving/loading

Running This

Standard PyTorch setup:

pip install torch torchvision
jupyter notebook

Then just open whatever notebook you want.

Notes

• Lots of checkpoint files and __pycache__ around, ignore those
• Some notebooks are exercises, some are complete solutions
• Models are saved locally, nothing cloud-based yet

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Learning in public. Feel free to browse but don't expect production-ready code.