TOP 20 Tensorflow interview questions

July 28, 2018 0 By admin
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Tensorflow interview questions

1. Objective
We saw a comprehensive understanding of what is Tensorflow, its procedures, how to create programs and different operations associated with it. Now we will cover some of the important and most frequently asked Tensorflow interview questions and answers. These TensorFlow Interview Questions will help both freshers and experienced to crack TensorFlow Interview.

So, let start the best TensorFlow Interview Questions and Answers.

TensorFlow Interview Questions
Most popular TensorFlow Interview Questions and Answers

2. 30 Mostly Asked TensorFlow Interview Questions & Answers
Here, we are providing you with some tricky TensorFlow interview questions and answers that will help TensorFlow beginners and professionals to crack the TensorFlow interview

Q1. What is Tensorflow?

TensorFlow is a machine learning library created by the Brain Team of Google and made open source in 2015. Basically, Tensorflow is a low-level toolkit for doing complicated math and it offers the users customizability to build experimental learning architectures, to work with them and to turn them into running software.

Follow the link to learn more about TensorFlow

Q2. What does the latest release of TensorFlow have to offer?

The latest release of TensorFlow is 1.7.0 and is available on www.tensorflow.org. It has been designed with deep learning in mind but applicable to a much wider range of problems.

Q3. What are Tensors?

Tensors are higher dimensional arrays which are used in computer programming to represent a multitude of data in the form of numbers. There are other n-d array libraries available on the internet like Numpy but TensorFlow stands apart from them as it offers methods to create tensor functions and automatically compute derivatives.

Q4. What is a TensorBoard?

TensorBoard, a suit of visualizing tools, is an easy solution to Tensorflow offered by the creators that lets you visualize the graphs, plot quantitative metrics about the graph with additional data like images to pass through it.

Follow the link to learn more about TensorBoard

Q5. What are the features of TensorFlow?

Tensorflow has APIs for Matlab, and C++ and has a wide language support. With each day passing by, researchers working on making it more better and recently in the latest Tensorflow Summit, tensorflow.js, a javascript library for training and deploying machine learning models has been introduced.

Follow the link to learn more about the TensorFlow Features

Q6. List a few advantages of TensorFlow?

It has platform flexibility
It is easily trainable on CPU as well as GPU for distributed computing.
TensorFlow has auto differentiation capabilities
It has advanced support for threads, asynchronous computation, and queue es.
It is a customizable and open source.
Q7. List a few limitations of Tensorflow.

Has GPU memory conflicts with Theano if imported in the same scope.
No support for OpenCL
Requires prior knowledge of advanced calculus and linear algebra along with a pretty good understanding of machine learning.
Let’s discuss advantages and disadvantages of TensorFlow in detail

Q8. What are TensorFlow servables?

These are the central rudimentary units in TensorFlow Serving. Objects that clients use to perform the computation are called Servables.

The size of a servable is flexible. A single servable might include anything from a lookup table to a single model to a tuple of inference models.

Q9. What do the TensorFlow managers do?

Tensorflow Managers handle the full lifecycle of Servables, including:

Loading Servables
Serving Servables
Unloading Servables
Q10. What are TensorFlow loaders?

Tensorflow Loaders are used for adding algorithms and data backends one of which is tensorflow itself. For example, a loader can be implemented to load, access and unload a new type of servable machine learning model.

TensorFlow Interview Questions and Answers for Freshers. Q- 1,2,3,4,5,6,7,8

TensorFlow Interview Questions and Answers for Experience. Q- 9, 10

Q11. What is deep speech?

Deep Speech developed by Mozilla is a TesnsorFlow implementation motivated by Baidu’s Deep Speech architecture.

Follow the link to learn more about Deep speech

Q12.What do you mean by sources in TensorFlow?

Sources are in simple terms, modules that find and provide servables. Each Source provides zero or more servable streams. One Loader is supplied for each servable version it makes available to be loaded.

Q13. How does TensorFlow make use of the python API?

Python is the most recognisable and “the main” language when it comes to TensorFlow and its development. The first language supported by TensorFlow and still supports most of the features. It seems as TensorFlow’s functionality first define in Python and then moved to C++.

Follow the link to learn more about TensorFlow API

Q14. What are the APIs inside the TensorFlow project?

The API’s inside TensorFlow are still Python-based and they have low-level options for its users such as tf.manual or tf.nnrelu which use to build neural network architecture. These APIs also use to aid designing deep neural network having higher levels of abstraction.

Q15. What are the APIs outside TensorFlow project?

TFLearn: This API shouldn’t be seen as TF Learn, which is TensorFlow’s tf.contrib.learn. It is a separate Python package.
TensorLayer: It comes as a separate package and is different from what TensorFlow’s layers API has in its bag.
Pretty Tensor: It is actually a Google project which offers a fluent interface with chaining.
Sonnet: It is a project of Google’s DeepMind which features a modular approach.
Q16. How does TensorFlow use the C++ API?.

The runtime of TensorFlow is written in C++ and mostly C++ is connected to TensorFlow through header files in tensorflow/cc. C++ API still is in experimental stages of development but Google is committed to working with C++.

Q17. In TensorFlow, what exactly Bias and Variance are? Do you find any similarity between them?

In the learning algorithms, Biases can consider as errors which can result in failure of entire model and can alter the accuracy. Some experts believe these errors are essential to enable leaner’s gain knowledge from a training point of view.

Q18. Can TensorFlow be deployed in container software?

Tensorflow can also use with containerization tools such as docker, for instance, it could use to deploy a sentiment analysis model which uses character level ConvNet networks for text classification.

Q19. What exactly Neural Networks are? What are the types of same you are familiar with?

Neural networks as the name suggests are a network of elemental processing entities that together make a complex form. There can be Artificial Neural Networks and Biological Neural Networks. The use of artificial neural networks is more common as they try to imitate the mechanics of the human brain.

Have a look at Recurrent Neural Network

Q20. What are the general advantages of using the Artifical Neural Networks?

They use to find answers to complex problems in a stepwise manner. All the information that a network can receive can easily be in any format. They also make use of real-time operations along with a good tolerance capability.

Let’s revise Convolutional Neural Network

TensorFlow Interview Questions and Answers for Freshers. Q- 11,12,14,15,16,19

TensorFlow Interview Questions and Answers for Experience. Q- 13,17,18,20