Deep Learning MCQ (Multiple Choice Questions) With Answers, Deep Learning Online Quiz

1 .

When did deep learning start?

A)

1943

B)

1962

C)

1978

D)

1989

Correct Answer : Option (A) : 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?

A)

Ilya Sutskever

B)

David Rumelhart

C)

Alex Krizhevsky

D)

Frank Rosenblatt

Correct Answer : Option (D) : 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?

A)

2

B)

3

C)

4

D)

5

Correct Answer : Option (B) : 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?

A)

SciPy

B)

Numpy

C)

Deep learning

D)

All of the above

Correct Answer : Option (C) : 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?

A)

hidden layer

B)

outer layer

C)

inner layer

D)

None of the above

Correct Answer : Option (C) : 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)

a more precise but slower update.

B)

a less precise but faster update.

C)

a less precise and slower update.

D)

a more precise and faster update.

Correct Answer : Option (A) : a more precise but slower update.

7 .

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

A)

Dropout

B)

Pooling

C)

Early stopping

D)

Data augmentation

Correct Answer : Option (B) : Pooling

8 .

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

A)

Protein structure prediction

B)

Detection of exotic particles

C)

Prediction of chemical reactions

D)

All of the above

Correct Answer : Option (D) : 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?

A)

It can be used for feature pooling

B)

It can help in dimensionality reduction

C)

It suffers less overfitting due to small kernel size

D)

All of the above

Correct Answer : Option (D) : All of the above

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

10 .

RNNs stands for?

A)

Recurrent Neural Networks

B)

Report Neural Networks

C)

Receives Neural Networks

D)

Recording Neural Networks

Correct Answer : Option (A) : 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.