A comprehensive hands-on repository documenting a step-by-step journey through the PyTorch ecosystem, from foundational tensors to state-of-the-art computer vision models.
- 🧠 Foundations: Deep dive into Tensors and Automatic Differentiation.
- 🖼️ Computer Vision: Hands-on with CNNs, Image Classification, and Segmentation.
- 🎯 Object Detection: Implementation of R-CNN and YOLO11.
- ⚡ Transfer Learning: Leveraging state-of-the-art pretrained models.
- 🧪 Practical Labs: 10+ interactive Jupyter notebooks ready for Google Colab.
| Module | Topic | Description | Try it Out |
|---|---|---|---|
| 01 | 📝 Basics | Introduction to PyTorch tensors, operations, and basic syntax. |  |
| 02 | 🔄 Autograd | Understanding torch.autograd and the power of automatic differentiation. |
|
| 03 | 👕 Classification | Building a multi-class classifier for the Fashion MNIST dataset. | |
| 04 | 🕸️ CNN Scratch | Implementing Convolutional Neural Networks from the ground up. | |
| 05 | 🏗️ Transfer Learning | Using torchvision pretrained models for instant image classification. |
|
| 06 | 🎨 Fine-tuning | Training on Caltech-256 and optimizing pretrained architectures. | |
| 07 | ✂️ Segmentation | Pixel-level classification with Semantic Segmentation techniques. | |
| 08 | 🔍 Detection (R-CNN) | Mastering object detection using Region-Based CNNs. | |
| 09 | 🎭 Mask R-CNN | Instance Segmentation: Detecting and masking objects individually. | |
| 10 | ⚡ YOLO11 | Real-time SOTA object detection with the YOLO11 architecture. | |
The easiest way to start is by clicking the "Open in Colab" badges in the table above. This requires zero local setup.
If you prefer running locally, clone the repo and install dependencies:
# Clone the repository
git clone https://github.com/ridash2005/pytorch-learn.git
# Navigate to the directory
cd pytorch-learn
# (Optional) Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install PyTorch (see https://pytorch.org/get-started/locally/)
pip install torch torchvision torchaudio.
├── Module_1.ipynb # PyTorch Basics
├── Module_2.ipynb # Autograd & Tensors
├── Module_3.ipynb # Classification Foundations
├── Module_4.ipynb # CNN Scratch Implementation
├── Module_5.ipynb # Basic Transfer Learning
├── Module_6(PART 1).ipynb # Advanced Transfer Learning
├── Module_6(PART 2).ipynb # Fine-tuning Strategies
├── Module_7.ipynb # Semantic Segmentation
├── Module_8.ipynb # Object Detection (R-CNN)
├── Module_9.ipynb # Instance Segmentation (Mask R-CNN)
└── Module_10.ipynb # YOLO11 Implementation
This learning path is inspired by high-quality community resources:
- PyTorch Bootcamp: A fantastic YouTube Playlist for deep learning.
- LearnOpenCV: Expert tutorials that bridge the gap between theory and code.
- OpenCV Course: Foundations that make computer vision accessible.
Created with ❤️ for the AI Community. Happy Coding!