9 Common Neural Network Interview Questions and How to Prepare

A neural network is a specialized machine learning algorithm that helps computers make decisions. Neural networks use processes similar to neurons in the brain, working together to identify issues, compare options, and find solutions.

Multiple industries use neural networks for purposes such as targeted marketing, financial predictions, medical diagnoses, quality control, and energy demand forecasting.

You can use your neural networking skills in roles related to machine learning, such as designing neural networks or building artificial intelligence (AI) tools to develop algorithms and AI techniques.

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.

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 the way 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 in one direction from input to output. 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 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.

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.

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 offer good performance on training data but fall short on other test data. This can be the result of issues such as noisy data, not having enough training data, or too complex models. 

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. You can talk through your thought process for hyperparameter tuning when trying to find the optimal hyperparameters for a particular project. 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 using training, validation, and testing data sets. In your interview, you can give examples of projects you’ve worked on that tested neural network models and 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 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. 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 the conversation you’ve had during the interview. You can also use this time to further clarify 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 the conversation you’ve had or topics covered during the interview that you would like to expand on.

Preparing for a neural network interview with Coursera

Learn more about neural networks, machine learning, and deep learning; refresh your skills; and boost your understanding of neural networks as you prepare for neural network interview questions on Coursera.

Consider the IBM AI Engineering Professional Certificate to brush up on Python programming, deep learning, and neural networks. You can also earn your Bachelor of Computer Science from the University of London or a Master of Science in Data Science from the University of Colorado, Boulder, all from home with Coursera.