- 01. Introducing Alexis
- 02. Applications of CNNs
- 03. Lesson Outline
- 04. MNIST Dataset
- 05. How Computers Interpret Images
- 06. MLP Structure & Class Scores
- 07. Do Your Research
- 08. Loss & Optimization
- 09. Defining a Network in PyTorch
- 10. Training the Network
- 11. Pre-Notebook: MLP Classification, Exercise
- 12. Notebook: MLP Classification, MNIST
- 13. One Solution
- 14. Model Validation
- 15. Validation Loss
- 16. Image Classification Steps
- 17. MLPs vs CNNs
- 18. Local Connectivity
- 19. Filters and the Convolutional Layer
- 20. Filters & Edges
- 21. Frequency in Images
- 22. High-pass Filters
- 23. Quiz: Kernels
- 24. OpenCV & Creating Custom Filters
- 25. Notebook: Finding Edges
- 26. Convolutional Layer
- 27. Convolutional Layers (Part 2)
- 28. Stride and Padding
- 29. Pooling Layers
- 30. Notebook: Layer Visualization
- 31. Capsule Networks
- 32. Increasing Depth
- 33. CNNs for Image Classification
- 34. Convolutional Layers in PyTorch
- 35. Feature Vector
- 36. Pre-Notebook: CNN Classification
- 37. Notebook: CNNs for CIFAR Image Classification
- 38. CIFAR Classification Example
- 39. CNNs in PyTorch
- 40. Image Augmentation
- 41. Augmentation Using Transformations
- 42. Groundbreaking CNN Architectures
- 43. Visualizing CNNs (Part 1)
- 44. Visualizing CNNs (Part 2)
- 45. Summary of CNNs
- 46. Introduction to GPU Workspaces
- 47. Workspace Playground
- 48. GPU Workspace Playground