Welcome to my PyTorch deep learning practice repo!
This is a daily log of everything I’m learning and building from the Zero to Mastery PyTorch course by Daniel Bourke.
The goal: build a strong foundation in deep learning — from basics to real-world projects.
| Day | Notebook | Topics Covered |
|---|---|---|
| D01 | D1_tensors_operations.ipynb |
Tensors, Tensor Creation, Operations, GPU usage, Shape Manipulation |
| D02 | day02_reproducibility_device_agnostic.ipynb |
Reproducibility, Random Seeds, Device-Agnostic Code (CPU/GPU), Best Practices, PyTorch Docs, Quickstart Overview |
| D03 | day03_revision_exercises_docs_quickstart.ipynb |
Revision of Tensors & Operations, Solved Exercises, Explored PyTorch Documentation, Followed PyTorch Quickstart Guide in detail |
This repository contains:
- 📚 Day-wise notebooks focused on PyTorch core topics
- 🔢 Hands-on code covering tensors, NNs, CNNs, training workflows, and more
- 🧪 Mini-experiments and projects as I progress
- ✅ Clear commit messages and organization for learning traceability
By the end of this summer, I aim to:
- 🧠 Be confident with PyTorch fundamentals
- 🏗️ Build and train real neural networks from scratch
- 🔬 Apply for AI research assistant/intern roles
- 🌱 Start contributing to open-source AI/ML projects
- Language: Python
- Framework: PyTorch
- Platform: Google Colab
- Version Control: Git + GitHub
This learning journey is based on:
🎓 Zero to Mastery – Deep Learning with PyTorch by Daniel Bourke
One of the most comprehensive (and free) PyTorch courses available.
📌 I post updates on Twitter/X
If you’re learning deep learning too — feel free to fork this repo and join the journey.
Let’s build together, one day at a time. 🔥!!