Top Interview Questions on Convolutional Neural Networks (CNN)

In our last article, we talked about Recurrent Neural Networks and Natural Language Processing. NLP helps computers understand human languages, analyse data in the form of texts, and process responses similar to humans. Today we will talk about another important type of neural network, which is Convolutional Neural Networks or CNN. CNN plays a crucial role in computer vision. 

Computer vision as the name suggests aims at helping computers decrypt visual data and over time learn to recognise images. In this process, CNN helps the computer in breaking down the image into pixels, perform mathematical operations on it and make predictions on what it is seeing. This is widely used in applications such as image and video recognition, medical imaging, object detection, and facial recognition

Each passing day, the demand and applications of computer vision are growing across businesses. To employ these technologies and build the right products, companies are actively looking for Data Scientists with knowledge of Deep Learning and experience in CNN. 

If you are interviewing for a Data Scientist position that involves deep learning and artificial intelligence-related tasks, you should expect some questions on CNN in your interview. In this article, we have prepared a list of the top 15 Data Scientist interview questions on CNN.  These questions will help you practice and prepare for your next Data Scientist interview. 

CNN Interview Questions for Data Scientists

  1. Why is CNN preferred over ANN for image data?
  2. What is the importance of the RELU activation function in CNN?
  3. Explain the use of the pooling layer in CNN.
  4. Explain the difference between valid padding and the same padding in CNN.
  5. Explain the role of a fully connected (FC) layer in CNN.
  6. What is the importance of parameter sharing 
  7. Explain the different types of Pooling. 
  8. What is the use of the convolution layer in CNN?
  9. What are the advantages of using CNN over DNN?
  10. How would you visualise features of CNN in an image classification task?
  11. What do you understand by shared weights in CNN?
  12. Explain the process of flattening?
  13. Can CNN be used to perform Dimensionality Reduction? If yes, how?
  14. Define the term sparsity of connections in CNN.
  15. List the hyperparameters of a pooling layer in CNN. 

Before your next Data Scientist and AI interview, we suggest you practice these questions and prepare answers. To practice more, check out our article on Top Machine Learning interview questions

If you are new to Data Science, check out our GCDAI course which helps you become a world-class Data Scientist in just 10-months. To know more, connect with us here

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