| Week 1 |
Oct 15 |
Introduction to neural networks for image classification |
Pixel_Proficiency_Week1 |
Pixel_Proficiency_1a_FCN.ipynb Pixel_Proficiency_1b_CNN.ipynb |
Another popular classification architecture: ResNet |
| Week 2 |
Oct 22 |
Model selection and evaluation |
|
Pixel_Proficiency_2_Evaluation.ipynb |
|
| Week 3 |
Oct 29 |
Model tuning and transfer learning |
Pixel_Proficiency_Week3 |
Pixel_Proficiency_3_Tuning.ipynb |
CNN parameters cheatsheet Dropout Article |
| Week 4 |
Nov 5 |
Self-supervised learning through autoencoders |
|
Pixel_Proficiency_4_self-supervised-autoencoders |
Midterm Survey |
| Week 5 |
Nov 12 |
Segmentation and object detection |
Pixel_Proficiency_Week5 |
Pixel_Proficiency_5a_Semantic_Segmentation.ipynb Pixel_Proficiency_5b_Object_Detection.ipynb |
Another popular segmentation architecture: DeepLab, KerasHub model |
| Week 6 |
Nov 19 |
Addressing common pitfalls |
Nicoleta_slides Valentina_slides |
Pixel_Proficiency_6_grad-CAM_demo.ipynb |
Final Survey |