20 Multiple Choice Questions on TensorFlow

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(Ytf.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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22nd April 2019

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20 Multiple Choice Questions on TensorFlow

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