Top 10 Tensorflow Interview Questions And Answers 2021

Tensorflow Interview Questions

Machine learning can be a very complex discipline but what is more difficult and daunting is the implementation of machine learning models. This is made easy by machine learning frameworks such as TensorFlow by Google. It gives ease to the process of training models, acquiring data, refining results, and serving predictions. A TensorFlow course can help you understand the mechanics of this machine learning framework better, but what it cannot do is prepare you for a job interview with TensorFlow. So, here is a brief intro about TensorFlow and the type of questions you can expect in its interview.

Brief Intro of Tensorflow

TensorFlow is an open-source library and end-to-end framework used for creating machine learning applications. It was created by the Google Brain team. It is mostly used for large-scale machine learning applications and numerical computations. It has a symbolic math library data that makes use of differentiable programming and dataflow to execute several tasks of training and inference of deep neural networks.

TensorFlow basically allows developers to build machine learning applications using a variety of libraries, tools, and community resources. TensorFlow uses Python to provide developers with a convenient and front-end API for building applications within the framework as well as executing these applications in C++ with high performance.

TensorFlow is the most common and famous deep learning library worldwide. It can train and even run deep neural networks for various purposes such as image recognition, handwritten digit classification, word embeddings, natural language processing, particle differential equations, and many more.

Top 10 TensorFlow Interview Questions

Here is a list of the top 10 most frequently asked interview questions for a job at TensorFlow to familiarize you with the type and quality of questions asked in the interview.

1. What is TensorFlow?

TensorFlow is an open-source library based on Python utilized for creating machine learning frameworks or applications. It was created by the Brain Team of Google and made open source in 2015. It is a low-level toolkit that performs complex mathematical problems. It also offers customizability to users to develop experimental learning architectures and offers help to the users in working with them and turning these architectures into running software.

2. What Are Tensors?

Tensors are used in computer programming and are higher multidimensional arrays that represent a horde of data in the form of numbers. There are many other libraries on the internet but TensorFlow offers various methods to create tensor functions and compute derivatives automatically. This makes it stand out from other n-d libraries like NumPy.

3. How Many Types Of Tensors Are There?

There are three Tensors that are used to create models of a neural network:

Constant Tensor – These tensors are used as constants. It creates a node that inputs a value and then does not change it.

Variable Tensor – These nodes provide their current value as an output. This means that even after multiple executions of the graph, they can still retain their original value.

Place Holder Tensor – These are more essential than variables and can assign data at a later time. In these nodes, the value is only fed at the time of execution.

4. What Is a TensorBoard?

It is a suite for visualizing various tools and offers an easy solution to TensorFlow. It lets you visualize the plot of quantitative metrics and graphs with various additional data that may pass through it. This additional data may be in the form of images as well.

5. What Are The Features Of TensorFlow?

TensorFlow supports a wide number of languages and has APIs for both C++ and Matlab. Researchers are constantly working to make more improvements in TensorFlow and develop it further. In the latest summit of TensorFlow, a javascript library, tensorflow.js was introduced, which helps in deploying and training machine learning models.

6. What Are The Advantages Of TensorFlow?

● It offers platform flexibility.
● It can be used for distributed computing as it is easily trainable on both GPU and CPU.
● It has capabilities such as auto differentiation.
● It offers advanced support for queues, threads, and asynchronous computation.
● It is both open source and customizable.

7. What are the limitations of TensorFlow?

● It is imported in the same scope; it has some GPU memory conflicts with Theano.
● It has no support for OpenCL.
● It requires a deep understanding of machine learning, advanced linear algebra, and calculus.

8. Which Client Languages Are Supported By TensorFlow?

TensorFlow supports various client languages but the best one among them is Python. Experimental interfaces such as C++, Java, and Go are also provided by TensorFlow. There is also language binding present in this open source community which offers support for several other languages such as C#, Ruby, Scala, and Julia.

9. How To Load Data Into TensorFlow?

Before training or starting a machine learning algorithm, loading data is the first step and there are two main ways to load data into TensorFlow.

Loading data in the memory – This is the easiest method as it lets you load data into the memory in just a single array. In this, one can write Python code unrelated to TensorFlow.

TensorFlow data pipeline – Built-in APIs are present in TensorFlow which help in loading the data, performing the operations, and feeding the machine learning algorithm to the machine easily. This is mostly used while dealing with larger datasets.

10. List Down The Common Steps To Most TensorFlow Algorithms.

● Importing and generating data and then setting up a data pipeline through the placeholders
● Using a computational graph to feed the data
● Evaluating output based on the loss function
● Modify variables using backpropagation
● Repeat the above steps until the stopping condition

Conclusion

These are some of the TensorFlow questions that will prepare you for a TensorFlow interview. A job at TensorFlow can be very rewarding, but the interview process to get the job is not that easy and requires intense preparation and deep knowledge. This article has discussed a few of the most asked questions about TensorFlow in the TensorFlow interview process. You can always take up a course online that includes real time TensorFlow projects to learn more about machine learning libraries like TensorFlow, but this article will help you prepare for the unnerving exam that an interview is.

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