Deep Learning with PyTorch

My step‑by‑step notes and practice code while learning Deep Learning with PyTorch. This repo includes:

• Concise, beginner‑friendly notes (Markdown)
• Executable Jupyter notebooks with code experiments
• Small projects/demos as I progress (e.g., MNIST, Food101)

The aim is to learn by doing: write small bits of code, visualize results, and summarize key ideas.

Quick link: See consolidated chapter notes in chapter_0_fundamental_notes.md.

Repository Structure

deep-learning-with-pytorch/
├─ README.md
└─ chapter_0_fundamentals/
	 ├─ chapter_0_fundamental_notes.md   # narrative notes
	 └─ 00_pytorch_fundamentals.ipynb    # hands-on fundamentals notebook

As new chapters are added, each will typically contain a notes file and one or more notebooks.

Environment & Setup

You can run everything locally or in Google Colab.

• Local (VS Code recommended):

  • Install Python 3.10+ and pip
  • Create a virtual environment (optional but recommended)
  • Install PyTorch following the instructions for your OS/GPU: https://pytorch.org/get-started/locally/
  • Install Jupyter support: pip install jupyter matplotlib pandas numpy

• Google Colab:

  • Open the notebook in Colab
  • Runtime → Change runtime type → set Hardware accelerator to GPU (optional)

How to Use

1. Read the Markdown notes in each chapter to get the concepts.
2. Open the paired notebook to run code, tweak values, and explore shapes/dtypes/devices.
3. Use the notes as a quick reference while experimenting in the notebook.

Tip: If you hit errors like “mat1 and mat2 shapes cannot be multiplied”, check tensor shapes and transpose/reshape as needed.

Contributing / Feedback

Suggestions, fixes, and learning tips are welcome. Feel free to open an issue or submit a PR with a short description of the change.

References

• PyTorch Docs: https://pytorch.org/docs/stable/
• Learn PyTorch (book/site): https://www.learnpytorch.io/
• PyTorch Forums: https://discuss.pytorch.org/