Hello guys, if you are preparing for Machine Learning Engineer interviews or a Data Science Interview and looking for popular TensorFlow interview Questions then you have come to the right place. Earlier, I have shared the best courses to learn TensorFlow and Machine Learning and in this article, we are going to share the frequently asked TensorFlow questions for Interviews. These questions cover key TensorFlow concepts and you can use them to revise TensorFlow before your interview. They are good for both screening and Telephonic rounds of interviews.
Almost every company somehow uses some services that use artificial intelligence to help them run their company and even in people's daily lives like the spam filtering in Gmail and the recommendation system in their Youtube and the banking fraud detection system when sending money to your relatives and friends.
Most of these services and developers are using a language called Python, which is so easy to learn and contains a lot of packages to make things easier for people to perform specific actions like analyzing data or create a machine learning model.
One of the famous libraries is TensorFlow, developed by Google and released as an open-source tool for making artificial intelligent devices. This article will show you the most 15 used questions for new people who are going to an interview for the job as a machine learning engineer.
15+ TensorFlow Interview Questions with Answers
Now that we know how important TensorFlow is for Machine Learning Engineers, Data scientists and AI Specialists, let's check out the frequently asked TensorFlow Interview Questions and their answers to quickly revise key TensorFlow Concepts.
1. What is Tensorflow
Tensorflow is an open-source package or library developed by Google and anyone under the Apache License 2.0, and it is used to create artificial intelligence and machine learning models.
2. What are Tensors?
Tensors are pretty similar to the arrays in any programming language, but they can be in a higher dimension (n-dimension), and this will depend on the dataset you are trying to deal with.
3. What is TensorBoard?
TensorBoard is a tool provided by Tensorflow and used for visualization and measuring the performance of your machine and deep learning models such as the accuracy and the loss and many other matrices.
4. What are the benefits of using Tensorflow?
1- Tensorflow is open-source and has a large community to support you.
2- Tensorflow can be run on any platform.
3- Tensorflow is a scalable framework so you can develop anything you want.
5. What are the limitation of TensorFlow?
1- Little bit complex for beginners to start using it.
2- Tensorflow only support the GPU of NVIDIA
3- Tensorflow is slow compared to other AI frameworks
6. What are the different types of Tensors?
When you perform any action using Tensorflow that involves the manipulation of Tensors, you will use mainly four types of Tensors: tf.Variable, tf.constant, tf.placeholder, tf.SparseTensor. If you want to learn more you can further check out these best TensorFlow courses from Udemy and Coursera.
7. How data is loaded in Tensorflow
There are two ways to load your data in Tensorflow: to load data into memory as a single array unit and to load data in the pipeline by using built-in APIs.
8. What does the Tensorflow manager do?
The Tensorflow manager is responsible for many things such as loading and unloading and also the lifetime management of all servable objects.
9. What are the programming languages supported in Tensorflow?
Tensorflow supports many programming languages, but the most used one among developers is Python because of its easiness. You can use it in Java, Go, Swift, JavaScript, and much more.
10. Name some products developed using Tensorflow
Tensorflow can create a machine learning and deep learning models, and that's why many companies use this framework to develop products like Speech recognition, object detection, self-driving cars, and more.
11. The differences between supervised & unsupervised learning?
Tensorflow can solve the problem of both supervised learning which is training the model on a labeled dataset, and unsupervised learning is training the model on unlabeled data.
12. What is overfitting in Tensorflow?
Overfitting happens when your machine learning model is overtrained on the data you've provided, so when you try to see how your model works on unseen data, it will perform so much worse than you expected.
13. What are neural networks?
As the name suggests, the neural network is some algorithm that mimics how the human brain works to perform actions like face recognition and object detections, and anything that requires artificial intelligence.
14. Can you use the TensorBoard without installing Tensorflow?
The simple answer is yes. You can directly install the TensorBoard using either conda or pip, then using it in a standalone mode with redacted features, and there are many plugins supported.
15. What is the activation function?
The activation function is an essential part of the deep learning model, and it decides whether the neuron should be activated or not. The purpose is to introduce non-linearity into the output of the neuron.
That's all about the popular TensorFlow questions and answers you can prepare for Machine Learning and Data Scientists Interviews. These questions can be asked during the interview, but that doesn't mean you will get all of them in the interview. You have to be good at using Tensorflow with good experience to be prepared for any tricky questions you may get during the interview. Good Luck!
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In case of any queries, you can drop them down in the comments and let someone else answer them; you can have a discussion too.
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