This repository documents my journey of learning PyTorch from the fundamentals to building deep learning models.
The notebooks contain:
- PyTorch fundamentals
- Tensor operations
- Automatic differentiation
- Building neural networks
- Computer vision
- Custom datasets and DataLoaders
- Model training and evaluation
Along the way, I experimented with the code, debugged issues, and added my own notes to better understand the concepts.
- 01 - PyTorch Fundamentals
- 02 - PyTorch Workflow
- 03 - Clasification of neural networks
- 04 - Computer Vision
More notebooks and projects will be added as I continue learning.