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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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Which of the following methods DOES NOT prevent a model from overfitting to the training set?
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In which neural net architecture, does weight sharing occur?
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What consist of Boltzmann machine?
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Which, if any, of the following propositions is true about fully-connected neural networks (FCNN)?
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Sentiment analysis using Deep Learning is a many-to one prediction task
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In CNN, having max pooling always decrease the parameters?
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Which of the following would have a constant input in each epoch of training a Deep Learning model?
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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?
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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 ?
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In which of the following applications can we use deep learning to solve the problem?
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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
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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?
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