RNNs stands for?

Deep Learning MCQ Quiz (Multiple Choice Questions And Answers)

• Which of the following is a subset of machine learning?
• How many layers Deep learning algorithms are constructed?
• The first layer is called the?
• CNN is mostly used when there is an?
• Which of the following is/are Common uses of RNNs?
• Which neural network has only one hidden layer between the input and output?
• RNNs stands for?
• Deep learning algorithms are _______ more accurate than machine learning algorithm in image classification
• Which of the following is well suited for perceptual tasks?
• Which of the following is/are Limitations of deep learning?
• 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?
• Which of the following statements is true when you use 1×1 convolutions in a CNN?
• 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?
• 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
• In which of the following applications can we use deep learning to solve the problem?
• Assume a simple MLP model with 3 neurons and inputs= 1,2,3. The weights to the input neurons are 4,5 and 6 respectively. Assume the activation function is a linear constant value of 3. What will be the output ?
• 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?
• Which of the following would have a constant input in each epoch of training a Deep Learning model?
• In CNN, having max pooling always decrease the parameters?
• Sentiment analysis using Deep Learning is a many-to one prediction task
• Which, if any, of the following propositions is true about fully-connected neural networks (FCNN)?
• What consist of Boltzmann machine?
• In which neural net architecture, does weight sharing occur?
• Which of the following methods DOES NOT prevent a model from overfitting to the training set?
• Assume that your machine has a large enough RAM dedicated to training neural networks. Compared to using stochastic gradient descent for your optimization, choosing a batch size that fits your RAM will lead to::

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