Questions

1. Why is the prediction on a translated image low when using traditional neural networks?
2. How is convolution done?
3. How are optimal weight values in a filter identified?
4. How does the combination of convolution and pooling help in addressing the issue of image translation?
5. What do the filters in layers closer to the input layer learn?
6. What functionality does pooling have that helps in building a model?

7. Why can't we take an input image, flatten it (as we did on the Fashion-MNIST dataset), and then train a model for real-world images?
8. How does data augmentation help in improving image translation?
9. In what scenario do we leverage collate_fn for DataLoaders?
10. What impact does varying the number of training data points have on the classification accuracy of the validation dataset?