Which ones of the following statements on Residual Networks are true? (Check all that apply.)

Which one of the statements is true regarding residuals in regression analysis? Mean of residuals is always 0 Mean of residuals is always < 0 Mean of residuals is always > 0 There is no such rule for residuals

Which of the following is true regarding residual in linear regression?The sum of residuals should be equal to zeroThe sum of residuals should be greater than zeroThe sum of residuals should be less than zeroThere is no such condition on the sum of residuals, it can be anything

Question 7Which of the following are true about the inception Network? (Check all that apply)

Which is false?Group of answer choicesOutput of a layer in the residual network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of a layer in the dense network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of layer 𝑙 in the dense network will be one of the inputs for layers 𝑖∈(𝑙+1,𝐿) where 𝐿 is the total number of layers.A problem of convolution network is that some features may get extracted earlier in the network, but could be useful later on. However, it is hard to keep track of previous non-modified outputs.

The motivation of Residual Networks is that very deep networks are so good at fitting complex functions that when training them we almost always overfit the training data. True/False?