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Tensorflow Interview Questions and Answers

Tensorflow Interview Questions and Answers

June 5th, 2019

If you are looking tomake a career in Tensorflow, then you are at the right place. Here, we have provided you the best Tensorflow interview questions and answers to excel in your interview process. Tensorflow market share is about 49.21% according to datanyze website, so you can clearly see that the demand for tensorflow is more compared to other machine learning libraries. Gangboard offers Tensorflow Interview Questions and answers that assist you in splitting your Tensorflow interview and procure dream vocation as Tensorflow Developer. It is very useful as it is fast, portable and has been mainly with google from long time now. Tensorflow is one of the best software machine learning libraries amongst the all as it is used by many developers working on Machine Learning Applications.

We have designed the best tensorflow interview questions for both beginners and professionals, these are mainly created for people who are appearing for interview on Machine Learning and its various libraries. These tensorflow interview questions and answers are helpful in mastering important concepts and also to prepare for the interviews. By gaining knowledge from these tensorflow interview questions and answers you will be able to crack your interview and make a better career as a tensorflow developer. So, what are you waiting for get ahead with these best tensorflow interview questions.

Q1. What is Tensorflow?

Answer: Tensorflow is a python based library used for creating machine learning applications. It is made up of two words tensor and flow, tensor is known as data representation for multidimensional array and flow means a series of operations performed on tensors.

Q2. What are the use cases of tensorflow?

Answer: Tensorflow is an important tool for deep learning, it has mainly 5 use cases, they are:

  • Text Based Applications
  • Voice/Sound Recognition
  • Time Series
  • Image Recognition
  • Video Detection

Q3. What are the important steps of tensorflow architecture?

Answer: There are three main steps in tensorflow architecture they are:

  • Preprocess the data
  • Build a model
  • Train and estimate the model

Q4. Where can you run a Tensorflow?

Answer: The hardware and software requirements can be classified into two types:
Development phase: In this phase the model is trained usually on a laptop or a desktop.
Inference phase: This is the next step of training, in this phase the model is made to run. It can be run on different platforms, they are:

  • Operating Systems like windows, OSX and Linux.
  • Cloud web service.
  • Mobile OS like IOS and Android.

Q5. What are the important algorithms Tensorflow supports?

Answer: The following are a list of algorithms supported by Tensorflow:

  • Deep learning classification
  • Linear classification
  • Linear regression
  • Boosted tree regression
  • Boosted tree classification
  • Deep learning classification

Q6. What is keras?

Answer: Keras is an Application Programming Interface (API) designed for humans and not machines. It follows best ways to minimize cognitive load. It is efficient in offering consistent and simple API’s, also reduces amount of user actions required for common use cases providing clear feedback.

Q7. What is TensorBoard?

Answer: Tensorboard is a stack of web apps for visualizing, inspecting and understanding the Tensorflow runs and graphs.

Q8. What is a pooling layer?

Answer: pooling layer is generally used in reducing the spatial dimensions and not depth, on a convolutional neural network model.

Q9. What are the advantages of using Tensorflow?

Answer: The following are the advantages of tensorflow:

  • Flexible for various platforms.
  • Simple to train on CPUs and GPUs for distributed computing.
  • Easily customizable and is open source.
  • Advanced support for asynchronous computation, queues and threads.
  • It has auto differentiation capabilities.

Q10. What is the difference between CNN and RNN?

Answer: The following table shows the differences between CNN and RNN:

Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)
Best suited for spatial data like images. Best suited for sequential data.
CNN is powerful when compared to RNN. RNN supports less featureset than CNN.
This network takes fixed type of  inputs and outputs. This network can manage arbitrary input and output lengths.
These are ideal for video and image processing. These are ideal for text and speech analysis.

Q11. What are Tensorflow servables?

Answer: Servables are nothing but central rudimentary units existing in tensorflow serving. The objects that clients utilize to perform are known as servables. The servable size is flexible. Each servable may have anything from a lookup table to an isolated model to a tuple of inference models.

Q12. What are sources in Tensorflow?

Answer: In basic terms, sources are modules which identify and provide servables. Every source provides none or many servable streams. A single loader is supplied for a single servable version it makes accessible to be loaded.

Q13. What are the products built using Tensorflow?

Answer: The following are the products built using tensorflow:

  • Nsynth
  • Giorgio Camthat
  • Hand writing
  • Teachable machine

Q14. What are benefits of Tensorflow over other libraries?

Answer: The following are the benefits of tensorflow over other libraries:

  • Scalability
  • Visualization of data
  • Debugging facility
  • Pipelining

Q15. What is deep speech?

Answer: Deep speech is a tensorflow implementation by mozilla inspired from Baidu deep speech architecture.

Q16. What are the various dashboards in Tensorflow?

Answer: The following are various types of dashboards in tensorflow:

  • Text
  • Image
  • Graph
  • Audio
  • Embedding
  • Distribution
  • Histogram

Q17. What are Tensorflow abstractions?

Answer: Tensorflow comes with abstraction libraries such as TF-Slim and kereas, offering simplified high-level access to Tensorflow. These abstractions help streamline the construction of data flow graphs.

Q18. What is statistical regression?

Answer: Machine learning applications analyze data to extract data and insight, this process is formally referred as statistical regression.

Q19. What are operations in Tensorflow?

Answer: Tensorflow offers us many operations that can be applied on Tensors. The operation in tensorflow is defined as passing values and assigning the output to another tensor.

Q20. What are placeholders?

Answer: Constants are something which allow us to give values while defining the tensor, placeholders allow us to give values while at runtime.

Q21. How can we create tensors from python objects?

Answer: With the helps of python objects we have the opportunity to create tensors such as numpy arrays and lists, using the tf. convert_to_tensor() operation.

Q22. What are variables in Tensorflow?

Answer: Variables in tensorflow are known as tensor objects which hold values that can be modified while the execution of the program.

Q23. What is the difference between tf.variable and tf.placeholder?

Answer: The tf.variable and tf.placeholder are almost similar to each other but, there are some differences:

tf.variable tf.placeholder
tf.variable defines variable values that are modified with period of time. tf.placeholder defines certain input data that does not change with period of time.
tf.variable requires an initial value at the time of definition. tf.placeholder does not require initial value at the time of definition.

Q24. What is the difference between type1 and type 2 errors?

Answer: Type 1 refers to a false positive value and the type 2 error refers to a false negative value.

Q25. What is important in machine learning based tensorflow, performance or accuracy?

Answer: It usually depends on the complete performance. Both of them weigh equal although accuracy is prominent in most of the models.

Q26. What is pruning in a decision tree?

Answer: Pruning refers to minimizing the overall complexity of the model to increase the accuracy. This process is strictly managed as the model may completely become useless.

Q27. What is a Recurrent Neural Network(RNN)?

Answer: RNN is a category of Artificial Neural Network where the connections among the nodes form a directed graph throughout the temporal sequence.

Q28. What is the  possibility of evaluating a logistic regression model for a user?

Answer: Necessarily, the users need to completely understand typical goals connected to the concepts. Some use cases should be considered for this approach.

Q29.  Which unit gives non-linearity to a neural network?

Answer: Rectified Linear unit provides non-linearity to a neural network.

Q30. What is an important feature of model capacity?

Answer: Model capacity increases as number of hidden layers increase.