How to choose neural network architecture

1. How do I choose a neural network architecture?
2. How do I choose a neural network layer?
3. How do you choose parameters for CNN architecture?

How do I choose a neural network architecture?

The most common approach seems to be to start with a rough guess based on prior experience about networks used on similar problems. This could be your own experience, or second/third-hand experience you have picked up from a training course, blog or research paper.

How do I choose a neural network layer?

If data is less complex and is having fewer dimensions or features then neural networks with 1 to 2 hidden layers would work. If data is having large dimensions or features then to get an optimum solution, 3 to 5 hidden layers can be used.

How do you choose parameters for CNN architecture?

CONV layer: This is where CNN learns, so certainly we'll have weight matrices. To calculate the learnable parameters here, all we have to do is just multiply the by the shape of width m, height n, previous layer's filters d and account for all such filters k in the current layer.