Deep learning

Q1. The number of nodes in the input layer is 10 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are

Q2. Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes (p1, p2..pk) such that sum of p over all n equals to 1?

Q3. Which of the following statements is true when you use 1×1 convolutions in a CNN?

Q4. The input image has been converted into a matrix of size 28 X 28 and a kernel/filter of size 7 X 7 with a stride of 1. What will be the size of the convoluted matrix?

Q5. Which of the following are universal approximators?

Q6. Which of the following would have a constant input in each epoch of training a Deep Learning model?

Q7. In a simple MLP model with 8 neurons in the input layer, 5 neurons in the hidden layer and 1 neuron in the output layer. What is the size of the weight matrices between hidden output layer and input hidden layer?

Q8. In which of the following applications can we use deep learning to solve the problem?

Q9. Which of the following neural network training challenges can be solved using batch normalization?

Q10. Which of following activation functions can’t be used as output layer to classify an image ?