Week 9 Summary & Week 10 Preview

What We Learned This Week

🛠️

Deep Learning Frameworks

TensorFlow/Keras and PyTorch essentials

🐛

Debugging Techniques

Tools and strategies for fixing ML bugs

📚

Version Control

Git best practices for ML projects

📂

Code Organization

Building maintainable ML systems

✅ Choose the right framework for your needs - PyTorch for research, TensorFlow for production
✅ Systematic debugging saves time - use proper tools and workflows
✅ Version control is crucial for ML - track code, data, and experiments
✅ Good code organization enables collaboration and scalability

🖼️

Image Classification

End-to-end pipeline implementation

🎯

Object Detection

YOLO and R-CNN architectures

🎨

Image Segmentation

Pixel-level understanding with U-Net

🚀

Real-World Applications

Medical imaging, autonomous vehicles, and more

Prepared by Dr. Gorkem Kar