Deep learning Exam Questions with Answers
1. What is deep learning? correct answer: - an area of machine learning,
focuses on deep artificial neural networks which are loosely inspired by brains.
-Application correct answer: computer vision, speech recognition, natural language processing. Deep
learning is a class of machine learning algorithms that correct answer:[10](pp199-200)
-use a cascade of multiple layers of nonlinear processing units for feature extraction and
transformation. Each successive layer uses the output from the previous layer as input.
learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.
learn multiple levels of representations that correspond to ditterent levels of abstraction; the levels form a
hierarchy of concepts.
2. Why are deep networks better than shallow ones? correct answer: There are
studies which say that both
shallow and deep networks can fit at any function, but as deep networks have several hidden layers often
of ditterent types so they are able to build or extract better features than shallow models with fewer
parameters.
3. What is a cost function? correct answer: A cost function is a measure of the accuracy of the
neural network with respect
to given training sample and expected output. It is a single value, nonvector as it gives the performance of
the neural network as a whole. It can be calculated as below Mean Squared Error function correct
answer:-
MSE=1ni=0n(Y^i-Yi)^2
Where Y^ and desired value Y is what we want to minimize.
4. What is a gradient descent? correct answer: Gradient descent is basically an optimization
algorithm, which is used to learn the value of parameters that minimizes the cost function.
-Optimization is correct answer:
Finding an alternative with the most cost ettective or highest achievable performance under the given
constraints, by maximizing desired factors and minimizing undesired ones. In comparison, maximization
means trying to attain the highest or maximum result or outcome without regard to cost or expense.
-Optimization is the most essential ingredient in the recipe of machine learning algorithms. It starts with
1/
8
, defining some kind of loss function/cost function and ends with minimizing the it using one or the other
optimization routine
It is an iterative algorithm which moves in the direction of steepest descent as defined by the negative
of the gradient. We compute the gradient descent of the cost function for given parameter and update
the parameter by the below formula correct answer:-
2/
8
1. What is deep learning? correct answer: - an area of machine learning,
focuses on deep artificial neural networks which are loosely inspired by brains.
-Application correct answer: computer vision, speech recognition, natural language processing. Deep
learning is a class of machine learning algorithms that correct answer:[10](pp199-200)
-use a cascade of multiple layers of nonlinear processing units for feature extraction and
transformation. Each successive layer uses the output from the previous layer as input.
learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.
learn multiple levels of representations that correspond to ditterent levels of abstraction; the levels form a
hierarchy of concepts.
2. Why are deep networks better than shallow ones? correct answer: There are
studies which say that both
shallow and deep networks can fit at any function, but as deep networks have several hidden layers often
of ditterent types so they are able to build or extract better features than shallow models with fewer
parameters.
3. What is a cost function? correct answer: A cost function is a measure of the accuracy of the
neural network with respect
to given training sample and expected output. It is a single value, nonvector as it gives the performance of
the neural network as a whole. It can be calculated as below Mean Squared Error function correct
answer:-
MSE=1ni=0n(Y^i-Yi)^2
Where Y^ and desired value Y is what we want to minimize.
4. What is a gradient descent? correct answer: Gradient descent is basically an optimization
algorithm, which is used to learn the value of parameters that minimizes the cost function.
-Optimization is correct answer:
Finding an alternative with the most cost ettective or highest achievable performance under the given
constraints, by maximizing desired factors and minimizing undesired ones. In comparison, maximization
means trying to attain the highest or maximum result or outcome without regard to cost or expense.
-Optimization is the most essential ingredient in the recipe of machine learning algorithms. It starts with
1/
8
, defining some kind of loss function/cost function and ends with minimizing the it using one or the other
optimization routine
It is an iterative algorithm which moves in the direction of steepest descent as defined by the negative
of the gradient. We compute the gradient descent of the cost function for given parameter and update
the parameter by the below formula correct answer:-
2/
8
