9 Common Neural Network Interview Questions and How to Prepare

Key takeaways

Be prepared for neural network interview questions on topics ranging from basics, such as neurons' structure, to specifics about neural network types.

• You may be asked to describe concepts such as the foundations of neural networks, neural network learning, and the data sets used in neural network models.

• When preparing for your neural network interview, come up with examples that illustrate your experience reviewing neural network models, solving a vanishing gradient problem, or working with a specific type of neural network.

• You can show your curiosity and engagement by asking questions about the company, team, or specific aspects of the role.

Explore common questions interviewers might ask about your experience with neural networks, so you’re prepared to answer them well and make a good impression. To build AI engineering skills and gain practical experience, consider enrolling in the IBM AI Engineering Professional Certificate program. In as little as four months, you can gain skills in computer vision, data science, fine-tuning, LLM application, and more. 

9 common neural network interview questions

Review these questions before your next interview to help you develop strong responses about your skills and experience with neural networking.

1. Explain artificial neurons’ basic structure.

What they’re really asking: Do you understand the inspiration for neural networking?

Neural networks are built to deliver machine-based processing inspired by how the human brain works, with neurons that interact to accomplish various tasks. Artificial neurons work together to solve a problem using neurons connected in three layers. These layers include:

• Input layer: This layer is where information enters the artificial neural network. This layer processes, analyzes, and categorizes data before moving it to the next layer.

• Hidden layer: This layer further processes the data through several levels to generate information before passing it to the next layer.

• Output layer: This layer completes the analysis. It can have one or multiple outputs depending on the complexity of the problem.

Other forms this question might take:

• Can you describe the foundation of neural networks?

• Explain the different layers of neural networks.

2. How do neural networks learn?

What they’re really asking: Do you understand how neural networks process information?

Neural networks learn by creating connections and adjusting the weights of those connections between neurons through training processes. By repeating these processes over and over, neural networks can recognize patterns.

Other forms this question might take:

• Describe neural network learning.

• How do neural networks discover patterns in data?

3. What are the different types of neural networks?

What they’re really asking: Do you have the skills to work with various neural network types?

The different neural network types to remember for an interview include:

• Feedforward: A feedforward neural network processes data from input to output in one direction. This network uses a feedback loop to optimize its predictions.

• Backpropagation algorithm: Backpropagation uses corrective feedback loops to improve predictive analytics.

• Convolutional neural networks: Convolutional neural networks use hidden layers to filter, summarize, and perform other mathematical functions. This neural network type is particularly helpful for image classification.

• Generative adversarial network: This type of network uses a generator and a discriminator against each other to process data like video and audio. The generator works to create the data while the discriminator authenticates the data that’s been generated.

Other forms this question might take:

• Describe your work experience with types of neural networks.

• Give an example of your success working with a type of neural network.

What are autoencoders?

Autoencoders are a type of neural network that compress input data to its basic features and then reconstruct an approximation of the original input from this compressed state in its output. 

4. What is the vanishing gradient problem?

What they’re really asking: Can you explain issues with neural network training?

The vanishing gradient problem can arise in neural network training when the gradients used to train the network become small or vanish during the backpropagation process.

You can answer this question in a way that shows you understand the vanishing gradient problem and know how to fix this issue. You can explain solutions such as setting up particular data weights at the start of the process, using batch normalization, or trying activation functions.

Other forms this question might take:

• Can you troubleshoot and fix vanishing gradient problems?

• Give an example of how you solved a vanishing gradient problem.

Read more: Sigmoid Activation Function: Deep Learning Basics

5. How do you handle overfitting?

What they’re really asking: Do you know how to troubleshoot model issues?

Overfitting occurs when neural network models take in all data, including any noise, rather than just the data needed for evaluation. Overfitted models perform well on training data but fall short on other test data. This can result from issues such as noisy data, insufficient training data, or models that are too complex. 

Potential employers may want to know about your skills in identifying when overfitting occurs, why it’s happening, and the techniques you have mastered to prevent it from occurring. You can use this question to talk about collecting adequate training data to reduce overfitting or using techniques like pruning to remove unnecessary branches of data that are causing overfitting to occur.

Other forms this question might take:

• When have you worked with overfitting issues?

• Do you have a preferred technique to deal with overfitting?

6. What are some of the hyperparameter tuning techniques in neural networks?

What they’re really asking: Can you adapt to different project variables?

Hyperparameters are variables that can be set before the machine learning process begins to help train a neural network model. When finding the optimal hyperparameters for a particular project, you can talk through your thought process for hyperparameter tuning. Review hyperparameter tuning techniques such as Bayesian optimization, grid search, and random search.

Other forms this question might take:

• How do you deal with different tuning techniques?

• What different tuning techniques are you familiar with?

7. How do you evaluate neural network models?

What they’re really asking: Do you know how to fix neural network performance?

You train and test neural network models with the goal of creating the optimal model based on your specific needs. You can do this by training, validating, and testing data sets. In your interview, you can give examples of projects you’ve worked on that tested neural network models, how you trained your models for optimal performance, any issues you encountered, and how you evaluated whether your models were successful. 

Other forms this question might take:

• Give an example of when you reviewed neural network models.

• Can you describe the data sets for neural network models?

8. How do you ensure you’re familiar with the latest advancements in neural network research?

What they’re really asking: Are you curious about learning?

The field of neural networking can be fast-paced and constantly evolving, so it’s essential to stay current with the latest trends and technology.

Consider taking courses on educational platforms like Coursera and from companies in the neural networking space like Amazon Web Services (AWS) and Google. Certifications can help to prove your proficiency and knowledge of neural networking. Some certifications to consider include the NVIDIA Deep Learning Institute (DLI) certifications and the Certified Deep Learning Expert Certification (CDLE) from the International Association of Business Analytics Certification (IABAC). Use this opportunity to highlight any courses you’ve taken, newsletters and tech blogs you read regularly, and other ways you stay up to date on the industry.

Other forms this question might take:

• Have you taken any neural network courses recently?

• How do you stay on top of changes in the field of neural networking?

9. What questions do you have for me?

What they’re really asking: Are you curious and engaged?

This question usually comes at the end of the interview. It can be a good time for you to demonstrate your interest in and knowledge of the company, and your conversation during the interview. You can also use this time to clarify further whether this is the right job and company for you.

Come to the interview prepared to ask questions about the company, the team you would be working with, or specific aspects of your potential role.

This can also be a good time to build on your conversation or topics covered during the interview that you would like to expand on.

Explore our free interview preparation resources

Explore career paths, assess your skills, and connect with resume guidance while browsing our Career Resources Hub. Or if you want to learn more about machine learning, check out these free resources:

• Hear from an insider: Becoming an AI Engineer: 7 Questions with an IBMer 

• Watch on YouTube: Your Machine Learning Roadmap: What to Learn and When

• Read our Career Chat issue: 6 Machine Learning Certificates + How to Choose the Right One For You

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