Chapter 4 - Introducing Convolutional Neural Networks

1. Why is the prediction on a translated image low when using traditional neural networks?
   All images were centered in the original dataset, so the ANN learned the task for only centered images.
2. How is Convolution done?
   Convolution is a multiplication between two matrices.
3. How are optimal weight values in a filter identified?
   Through backpropagation.
4. How does the combination of convolution and pooling help in addressing the issue of image translation?
   While convolution gives important image features, pooling takes the most prominent features in a patch of the image. This makes pooling a robust operation over the vicinity, i.e., even if something is translated by a few pixels, pooling will still return the expected output.
5. What do the filters in layers closer to the input layer learn?
   Low-level features like edges.
6. What functionality does pooling do that helps in building a model?
   It reduces input size by reducing feature map size and...