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Deep learning

Deep learning

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Q1. 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?
Q2. Which of the following statements is true when you use 1×1 convolutions in a CNN?
Q3. Which of the following neural network training challenges can be solved using batch normalization?
Q4. ________ is the study of computer algorithms that learn through data
Q5. Which layer is the Input layer?
Q6. What are the layers in between called?
Q7. CNN
Q8. Number of layers of deep learning algorithm
Q9. Uses of RNN
Q10. Which of following activation functions can’t be used as output layer to classify an image ?
Q11. In which of the following applications can we use deep learning to solve the problem?
Q12. 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
Q13. CNNs are suited for
Q14. Deep learning teaches computer to
Q15. Which of the following would have a constant input in each epoch of training a Deep Learning model?
Q16. 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?
Q17. 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?
Q18. Deep learning is
Q19. Which of the following are universal approximators?
Q20. ____________ is a multi-layered neural network that can store information in context nodes

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