Top Deep Learning Interview Questions (With Sample Answers)

Indeed Editorial Team

Updated 14 March 2023

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Machine learning and artificial intelligence are emerging tech employment fields for IT professionals. Deep learning is a type of machine learning, and many organisations hiring candidates for deep learning roles conduct comprehensive interviews to assess their technical, analytical and problem-solving skills. If you are preparing for a deep learning interview, revising answers to some commonly asked questions can increase your chances of success. In this article, we share some frequently-asked deep learning interview questions alongside their samples answers for your review.

Types of deep learning interview questions

Here are the common types of deep learning interview questions that recruiters and hiring managers may pose during an interview:

Basic deep learning questions

Such questions usually help the interviewer assess your understanding of fundamental concepts and topics in deep learning. Here are some examples:

  • Define deep learning and neural networks.

  • Explain perception with an example.

  • What is the importance of data normalisation in deep learning?

  • What is a multi-layer perceptron (MLP)?

  • Define hyperparameters and discuss some common ones.

  • Explain cost function and gradient descent.

  • Define a feedforward neural network and a recurrent neural network with examples.

  • Explain the importance of activation functions in neural networks.

  • What is the Boltzmann machine?

  • Discuss backpropagation and its benefits in deep learning.

Related: 60 Machine Learning Interview Questions (With Sample Answers)

Deep learning questions about professional experience

These questions help recruiters and hiring managers to understand your professional experience, qualifications and specialisation in deep learning:

  • Explain some deep learning platforms and programmes you are proficient in.

  • How can deep learning solutions help businesses improve efficiency?

  • What are the different programming languages and tools that you know?

  • Explain the application of deep learning solutions to solve real-world problems.

  • Explain some of the most common challenges and obstacles deep learning engineers face when collecting or processing data.

  • How do you imagine deep learning influencing technological innovation and society in the next decade?

  • Do you think an academic qualification is necessary to become a deep learning expert?

  • How would you explain deep learning to someone who does not understand technology?

  • Have you worked on a deep learning programme that solved a specific business challenge?

  • If you had to design a week-long deep learning crash course curriculum, which subjects would you choose and why?

Related: What Is Deep Learning AI? (Benefits, Limitations And Techniques)

Advanced deep learning questions

In-depth deep learning questions enable interviewers to evaluate your expertise and knowledge level in deep learning:

  • Explain the importance of weight initialisation in a neural network.

  • Between shallow and deep networks, which one do you think are better?

  • Discuss the difference between supervised and unsupervised algorithms.

  • What is feature extraction, and why is it required?

  • What is a deep learning model, and how do you deploy one?

  • Explain the utility of Softmax and ReLU functions.

  • Discuss how batch gradient descent and stochastic gradient descent are different.

  • Define the different layers of a convolutional neural network.

  • What is a long-short-term memory (LSTM), and how does it function?

  • Explain how epoch, batch and iteration are different.

  • What is Tensorflow, and why is it preferred?

Related: What Is TensorFlow? (Components, Applications And Jobs)

Sample deep learning interview questions and answers

Here are some sample deep learning questions and answers to help you prepare for your next interview:

What is the application of deep learning in the real world?

Interviewers usually ask this question to understand if you know the practical application of deep learning. This question also tests your critical and analytical thinking skills. While answering such a question, explain different examples of deep learning uses and benefits.

Example answer: "There are several applications and practices of deep learning in all types of organisations and industries. From the face-recognition software in our devices to testing the efficacy of new drugs and medicines, deep learning solutions can help businesses and organisations improve their services and products. Deep learning is also extremely useful in personalising customer experiences when they interact with applications and web systems by recommending relevant suggestions and prompts."

Related: Guide: How To Become An Artificial Intelligence Engineer

Define and explain a neural network in deep learning.

A neural network is a fundamental concept in deep learning. Deep learning engineers and IT professionals require a thorough understanding of all deep learning components, including neural networks, their utility and types. While answering this question, define neural networks and explain them with an example.

Example answer: "Neural networks receive inputs, perform calculations and use the output to solve specific problems. Neural networks are important in different applications and solutions. For example, they help in classification. Like other classifiers, such as random forest, decision trees and logistic regression, neural networks are important when classifying data into labelled classes or categories."

Related: Deep Learning Vs Machine Learning: What Are The Differences?

Define overfitting and underfitting, and how do you manage them?

This is another question to assess your knowledge and skills in deep learning. It also helps evaluate your problem-solving abilities. Explain both these concepts, and then discuss how you will handle and resolve them.

Example answer: "Overfitting is when the model also learns the noise in the training data to an extent where its execution of new information is impacted adversely. This usually occurs in non-linear models with more flexibility when they are learning target functions. For instance, if we teach a model to recognise doors from looking at doors and windows, it might not recognise a door made of glass as it is learning only one type of door during training. This sort of model may function well with training data but might not be successful in real-world applications.

Underfitting is when a model can neither generalise to new information nor is well-trained on data. This is usually the result of using inaccurate data or fewer data points to train a model. Underfitting can significantly impact the accuracy and performance of the model. Re-sampling the data to establish its accuracy is essential to avoid underfitting and overfitting. This also helps estimate the accuracy of the model and evaluate its performance."

Which one do you think is more important between model performance and model accuracy?

This question helps interviewers assess your ability to design deep learning models by considering their components. While both performance and accuracy are important, ensuring accuracy takes precedence in deep learning. You can explain your view in the answer by discussing the importance of accuracy.

Example answer: "It is important to maintain both performance and accuracy for any successful deep learning model. While both these are dependent on the specific application, ensuring accuracy is usually more important. This is because if the model does not give accurate results and information, the performance, or speed, with which it operates holds no value. So, model accuracy is extremely vital for any deep learning model."

Discuss the ethical scope and ramifications of using deep learning solutions.

This is a general deep learning question to assess your knowledge and opinion on the implications of deep learning technology. Since these technologies and solutions are relatively new, concerns regarding data privacy, algorithm biases, and consent of data usage are prevalent. You can explain these issues briefly and offer a potential solution to answer his question.

Example answer: "Using deep learning solutions ethically is important to ensure that they benefit people and society. For instance, disregarding concerns related to privacy can lead to extremely challenging consequences that may be detrimental to innovation and development. As artificial intelligence and machine learning algorithms learn more, we have to be aware of the unconscious biases and discrimination that they are learning. A collaboration between tech innovators, businesses, legislators and academicians is necessary to understand the scope of these new technologies and how to use them for betterment."

Related: A Complete Guide To Reinforcement Learning (With Types)

Define data augmentation.

Data augmentation is an important concept in deep learning, and interviewers seek to understand your expertise by asking this type of question. Make sure that in addition to defining it, you explain the different techniques of augmentation while answering this question.

Example answer: "Data augmentation is the creation of new data by improving the size and quality of training datasets. This helps ensure build models with higher accuracy that offer better quality results. There are many different approaches to augment data, such as numerical data augmentation, image data augmentation, GAN-based augmentation and text augmentation."

What components would you use to train hyperparameters in a neural network?

This question helps assess your expertise in hyperparameters and your ability to implement theoretical knowledge. It is also a way to understand your critical thinking abilities. To answer this question, explain the four components that you can use to train hyperparameters.

Example answer: "You can train hyperparameters using four components. These include the batch size, or the size of input data and epochs, or the number of times training data becomes visible to neural networks. The other two components are momentum, or the next step of data execution and the learning rate, or the time necessary for the network to update different parameters and learn."

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