Deep Learning questions

Post A Question

Classifications:

Other Categories:

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::
icon 1 answers
icon194 Views
icon0 Likes
Which of the following methods DOES NOT prevent a model from overfitting to the training set?
icon 1 answers
icon255 Views
icon0 Likes
In which neural net architecture, does weight sharing occur?
icon 1 answers
icon249 Views
icon0 Likes
What consist of Boltzmann machine?
icon 1 answers
icon251 Views
icon0 Likes
Which, if any, of the following propositions is true about fully-connected neural networks (FCNN)?
icon 1 answers
icon289 Views
icon0 Likes
Sentiment analysis using Deep Learning is a many-to one prediction task
icon 1 answers
icon442 Views
icon0 Likes
In CNN, having max pooling always decrease the parameters?
icon 1 answers
icon232 Views
icon0 Likes
Which of the following would have a constant input in each epoch of training a Deep Learning model?
icon 1 answers
icon314 Views
icon0 Likes
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?
icon 1 answers
icon233 Views
icon1 Likes
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 ?
icon 1 answers
icon280 Views
icon0 Likes
In which of the following applications can we use deep learning to solve the problem?
icon 1 answers
icon263 Views
icon0 Likes
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
icon 1 answers
icon291 Views
icon0 Likes
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?
icon 1 answers
icon236 Views
icon0 Likes