Which of the following architecture solved the vanishing gradient problem by allowing the gradient to bypass different layers to improve performance?1 pointResNetVGGNetImageNetAlexNet

Which of the following are types of CNN architecture? Check all that apply:1 pointJoNetRavNeVGGNetAlexNet

Gradient descent 1 pointoptimizes the weights of a neural networkoptimizes the loss function of a neural networkoptimizes the activation function of a neural networkNone of the above

During the training of RNNs for sequence generation, what is the common technique used to mitigate the vanishing gradient problem?DropoutGradient clippingData augmentationL1 regularizationBatch normalization

give method to fight vanishing gradient in fully connected neural networks. assume we are using a network with sigmoid activations trained using SGD. you are designing a deep learning system to diagnose chest cancer through X ray images. what's the most appropriate metric and why: accuracy, precision, recall, f1 score

Consider the following neural network model or class:1234567891011class Net(nn.Module):    def __init__(self,D_in,H,D_out):        super(Net,self).__init__()        self.linear1=nn.Linear(D_in,H)        self.linear2=nn.Linear(H,D_out)             def forward(self,x):        x=torch.sigmoid(self.linear1(x))          x=torch.sigmoid(self.linear2(x))        return xHow many hidden neurons does the following neural network object have?1model=Net(1,6,1)