Deep Learning MCQs with Answers

Introduction :

Unlock your potential in the cutting-edge field of Deep Learning with our extensive collection of Multiple Choice Questions (MCQs) and detailed answers! As Deep Learning continues to drive advancements in AI, from natural language processing to computer vision, a strong grasp of its principles is paramount. This resource is designed for aspiring AI engineers, data scientists, and enthusiasts eager to test their knowledge of neural network architectures, backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and popular frameworks. Whether you're gearing up for a tech interview, preparing for an academic examination, or simply aiming to deepen your understanding, our meticulously crafted MCQs provide an excellent pathway to solidify your Deep Learning expertise.

1 .

When did deep learning start?

1943

1962

1978

1989

Correct Answer : 1943

Explanation : The history of deep learning dates back to 1943 when Warren McCulloch and Walter Pitts created a computer model based on the neural networks of the human brain.

2 .

Who is father of deep learning?

Ilya Sutskever

David Rumelhart

Alex Krizhevsky

Frank Rosenblatt

Correct Answer : Frank Rosenblatt

Explanation : We conclude that Frank Rosenblatt developed and explored all the basic ingredients of the deep learning systems of today, and that he should be recognized as a Father of Deep Learning, perhaps together with Hinton, LeCun and Bengio who have just received the Turing Award as the fathers of the deep learning revolution.

3 .

How many layers Deep learning algorithms are constructed?

Correct Answer : 3

Explanation : Deep learning algorithms are constructed with 3 connected layers :
* inner layer
* outer layer
* hidden layer

4 .

Which of the following is a subset of machine learning?

SciPy

Numpy

Deep learning

All of the above

Correct Answer : Deep learning

Explanation : Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning.

5 .

The Deep Learning first layer is called the?

hidden layer

outer layer

inner layer

None of the above

Correct Answer : inner layer

Explanation : The first layer is called the Input Layer. The last layer is called the Output Layer. All layers in between are called Hidden Layers.

6 .

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::

a more precise but slower update.

a less precise but faster update.

a less precise and slower update.

a more precise and faster update.

Correct Answer : a more precise but slower update.

7 .

Which of the following methods DOES NOT prevent a model from overfitting to the training set?

Dropout

Pooling

Early stopping

Data augmentation

Correct Answer : Pooling

8 .

In which of the following applications can we use deep learning to solve the problem?

Protein structure prediction

Detection of exotic particles

Prediction of chemical reactions

All of the above

Correct Answer : All of the above

Explanation : We can use neural network to approximate any function so it can theoretically be used to solve any problem.

9 .

Which of the following statements is true when you use 1×1 convolutions in a CNN?

It can be used for feature pooling

It can help in dimensionality reduction

It suffers less overfitting due to small kernel size

All of the above

Correct Answer : All of the above

Explanation : 1×1 convolutions are called bottleneck structure in CNN.

10 .

RNNs stands for?

Recurrent Neural Networks

Report Neural Networks

Receives Neural Networks

Recording Neural Networks

Correct Answer : Recurrent neural networks

Explanation : Recurrent neural networks (RNNs) : RNN is a multi-layered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence.

11 .

Which of the following is well suited for perceptual tasks?

Reinforcement Learning

Recurrent Neural Networks

Convolutional Neural Networks

Feed-forward Neural Networks

Correct Answer : Convolutional Neural Networks

Explanation : Convolutional Neural Networks(CNN) is a multi-layered neural network with a unique architecture designed to extract increasingly complex features of the data at each layer to determine the output. CNNs are well suited for perceptual tasks.

12 .

CNN is mostly used when there is an?

structured data

unstructured data

Both A and B

None of the above

Correct Answer : unstructured data

Explanation : CNN is mostly used when there is an unstructured data set (e.g., images) and the practitioners need to extract information from it.

13 .

Which neural network has only one hidden layer between the input and output?

Deep neural network

Shallow neural network

Recurrent neural networks

Feed-forward neural networks

Correct Answer : Shallow neural network

Explanation : Shallow neural network : The Shallow neural network has only one hidden layer between the input and output.

14 .

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 :

Less than 50

More than 50

It is an arbitrary value

Correct Answer : 50

Explanation : Since MLP is a fully connected directed graph, the number of connections are a multiple of number of nodes in input layer and hidden layer.

15 .

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?

20x20

21x21

22x22

25x25

Correct Answer : 22x22

Explanation : The size of the convoluted matrix is given by C=((I-F+2P)/S)+1, where C is the size of the Convoluted matrix, I is the size of the input matrix, F the size of the filter matrix and P the padding applied to the input matrix. Here P=0, I=28, F=7 and S=1. There the answer is 22.

16 .

What consist of Boltzmann machine?

stochastic update

asynchronous operation

fully connected network with both hidden and visible units

All of the Above

Correct Answer : All of the Above

17 .

Which, if any, of the following propositions is true about fully-connected neural networks (FCNN)?

In a FCNN, there are connections between neurons of a same layer.

A FCNN with only linear activations is a linear network.

In a FCNN, the most common weight initialization scheme is the zero initialization, because it leads to faster and more robust training.

None of the above

Correct Answer : None of the above

18 .

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?

[1 X 5] , [5 X 8]

[5 x 1] , [8 X 5]

[8 X 5] , [5 X 1]

[8 X 5] , [ 1 X 5]

Correct Answer : [5 x 1] , [8 X 5]

Explanation : The size of weights between any layer 1 and layer 2 Is given by [nodes in layer 1 X nodes in layer 2].

19 .

Which of the following is/are Common uses of RNNs?

Provide a caption for images

Detect fraudulent credit-card transaction

BusinessesHelp securities traders to generate analytic reports

All of the Above

Correct Answer : All of the Above

Explanation : All of the above are Common uses of RNNs.

20 .

Which of the following is/are Limitations of Deep Learning?

Data Labeling

Obtain Huge Training Datasets

Both A and B

None of the above

Correct Answer : Both A and B

Explanation : Both A and B are Limitations of Deep Learning.

21 .

Deep Learning algorithms are _______ more accurate than machine learning algorithm in image classification.

41%

37%

31%

28%

Correct Answer : 41%

Explanation : Deep Learning can outperform traditional method. For instance, deep learning algorithms are 41% more accurate than machine learning algorithm in image classification, 27% more accurate in facial recognition and 25% in voice recognition.