APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
1
|
|
|
≥ 50
|
|
|
10
|
|
|
no limit
|
Detailed explanation-1: -There is no maximum number of layers in a deep network. You can increase the number of layers as much as you want. However, the big problem is that you need to feed this model with more samples, since the network capability increased.
Detailed explanation-2: -More than three layers (including input and output) qualifies as “deep” learning.
Detailed explanation-3: -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.
Detailed explanation-4: -While there is no limit to how many input layers you can have in a neural network, you need to have at least one for it to function.