Im new to deep learning and still learning on how to train my neural networks from the scratch. Sometimes I watch tutorial on YouTube or even online courses on the MOOC platforms. The base of a Convolutional Neural Networks usually has Conv2D and MaxPooling layers to make the input much more smaller and easy to be trained.
The thing is sometimes this tutorial online use the setup of Conv2D with higher number of neurons follows with other Conv2D with smaller one. Such as 1st layer is Conv2D(512...), 2nd layer is Conv2D(256....) and etcetera. Another tutorial setup is the increasing one, such as starts with Conv2D(16....), then Conv2D(32....) and increasing. These teacher doesn't tell us why they code the setup as so.
How do we know which setup to use? Is there any differences between them? Can't find on the net how to refer this case. If there's a paper that already described this case I would like to have it thanks.