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20 Multiple Choice Questions on TensorFlow

Knowledge test and Interview questions

Python, R and SAS work really well for solving the predictive modelling and machine learning problems. The libraries like “sklearn” are sufficient for building regression models, trees, random forest and boosting models. But these tools have limited deep neural networks libraries. What are the tools/frameworks for deep learning algorithms?

TensorFlow – One of the most famous deep learning framework.

  • TensorFlow was developed by the Google Brain team for internal use.
  • It was released under the Apache 2.0 open source license on November 9, 2015
  • Most popular among all Deep learning frameworks.
  • TensorFlow works really well with matrix computations – All the deep learning algorithms are highly calculation intensive.
  • Scalable to multi-CPUs and even GPUs
  • Can handle almost all type of deep networks, be it ANN or CNN or RNNs
  • Has Python API and python is very easy install and to work on.
  • We can use numpy to build all the models from scratch. But TensorFlow does it better by providing function to do it easily.
  • TensorFlow has one of the best documentation and great community support as of now.
  • Keras is a wrapper on top of TensorFlow. High level API written in Python. Less lines of code

Below is a list of Interview questions on TensorFlow and Keras

 

 

Sr No

Question

Option A

Option B

Option C

Option D

Answer

1

Which tool is best suited for solving
Deep Learning problems

R

Sk-learn

Excel

TensorFlow

D

2

A tensor is similar to

Data Array

ANN Model

SQL query

Pythoncode

A

3

How calculations work in TensorFlow

Through vector multiplications

Through RDDs

Through Computational Graphs

Through map reduce tasks

C

4

In TensorFlow, what is the used of a
session?

The current work space session for storing
the code

We launch the graph in a session

A session is used to download the data

A session is used for exporting data out of
TensorFlow

B

5

What does feed_dict do?

Feeds external data into computational
graphs

Creates a new place holder

Creates a new tensor

Creates a new session

A

6

out=tf.add(tf.matmul(X,W), b)

Logistic Regression Equaltion

Deep ANN equation

Random Forest Equation

Linear Regression equation

D

7

tf.reduce_sum(tf.square(out-Y))

Linear Model equation

Maximum Entropy loss function

Squared Error loss function

Feed_dict process

C

8

How can we improve the calculation speed in TensorFlow, without losing accuracy?

Using GPU

By doing random sampling on Tensors

By removing few nodes from computational
graphs

by removing the hidden layers

A

9

Kears is a deep learning framework on which tool

R

TensorFlow

SAS

Azure

B

10

What is the meaning of model=sequentil() in Keras?

No such code in Keras

Keras should be used only for sequential
models like RNNs

Keras builds sequential models

creates a computational graph

C

11

Which tool is NOT Suited for building
ANN models

Python

TensorFlow

Excel

Keras

C

12

Can we have multidimentional tensors

No tensor can have maximum two dimentions

Possible only in image data

Yes possible

Possible only in geo tagged data

C

13

Why Tensorflow uses computational
graphs?

Tensors are nothing but computational
graphs

Gaphs are easy to plot

There is no such concept of computational
graphs in TensorFlow

Calculations can be done in parallel

D

14

How do we perform caculations in
TensorFlow?

We launch the computational graph in a
session

We launch the sesssion inside a
computational graph

By creating multiple tensors

By creating data frames

A

15

How do you feed external data into
placeholders?

by using impoar data command

by using feed_dict

by using read data function

Not possible

B

16

out=tf.sigmoid(tf.add(tf.matmul(X,W),
b))

Logistic Regression Equaltion

Deep ANN equation

Random Forest Equation

Linear Regression equation

A

17

C=-tf.reduce_sum(Y*tf.log(out))

C is a logistc regression line equation

C is a squared error loss function

C is a cross entropy loss functiom

C is a linear regression line equation

C

18

Can we use GPU for faster computations in TensorFlow

No, not possible

Possible only on cloud

Possible only with small datasets

Yes, possible

D

19

Which tool is a deep learning wrapper on TensorFlow

Python

Keras

PyTourch

Azure

B

20

How deep learning models are built on Keras

by using sequential models

by using feed_dict

by creating place holders and computational
graphs

by creating data frames

A

 

 

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statinfer app – A new way to learn and test your knowledge on data science

https://play.google.com/store/apps/details?id=com.learn.statinfer1

A fun way of learning data science. App contains interview questions

 

22nd April 2019

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