Deep Learning
Summary of the Deep Learning test
This Deep Learning (DL) test evaluates candidates’ knowledge and ability to solve situational tasks using DL. This screening test will help you hire developers who are proficient in DL and capable of using it to solve business problems.
Covered skills
Mathematical foundations of deep learning
General neural network architecture and concepts
Neural network optimization and regularization
Image processing with neural networks
Use the Deep Learning test to hire
Deep learning engineers, machine-learning engineers, artificial intelligence engineers, data analysts, and any other roles requiring intermediate knowledge of deep learning.
About the Deep Learning test
In our age of information and technologies, any business deals with massive data. It is crucial to efficiently get insights about it to make various decisions, and deep learning can help to solve that task, saving the time and resources required for traditional analysis techniques.
Deep Learning provides efficient solutions for dealing with different types of data — it is able to get meaningful insights about it and even make predictions for future values. Questions in this test focus on different scenarios when working with data, and DL provides functionality for dealing with all of them. Questions in the test are related to all core DL models included in supervised, unsupervised, and reinforcement learning techniques.
Candidates who perform well on the test must have a fundamental knowledge of core Deep Learning approaches and can make the best use of each one when working with different types of information. The test assures that candidates have all the necessary skills to help the company achieve its goals.
The test is made by a subject-matter expert
TestGorilla’s tests are created by subject matter experts. We assess potential subject-matter experts based on their knowledge, ability, and reputation.Before being published, each test is peer-reviewed by another expert, then calibrated using hundreds of test takers with relevant experience in the subject.
Our feedback mechanisms and unique algorithms allow our subject-matter experts to constantly improve their tests.
Gary R.
Gary has been working in the data science field for more than three years and is proficient in the fields of machine learning and data analysis. He has a Bachelor’s degree in Economics and a Master’s degree in Computer Science. The combination of those two fields helps Gary to achieve even greater results. He is fond of computer science and loves to work on projects related to Artificial Intelligence which is, in his opinion, the future of our world.
Reliability: Cronbach’s alpha coefficient = .64
Face validity: Candidates rated this test as accurately measuring their skills (average score of 3.61 out of 5.00).
Criterion-related validity: Candidates with higher scores on this test received higher average ratings from the hiring team during the selection process (r = .29, N = 62).
For an in-depth look at interpreting test results, please take a look at our Science series articles: How to interpret test fact sheets (part 1): Reliability, and How to interpret test fact sheets (part 2): Validity.
For an explanation of the various terms, please refer to our Science glossary.
Reliability and validity | Sufficient data available | Analyses and checks conducted | Outcome |
|---|---|---|---|
Reliability | ✔ | ✔ | Acceptable |
Content validity | ✔ | ✔ | Acceptable |
Face validity | ✔ | ✔ | Acceptable |
Construct validity | ✔ | ✔ | Acceptable |
Criterion-related validity | ✔ | ✔ | Acceptable |
Group differences | |||
Age differences | Pending | Pending | Pending |
Gender differences | ✔ | ✔ | Acceptable |
Ethnicity differences | Pending | Pending | Pending |
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The Deep Learning test will be included in a PDF report along with the other tests from your assessment. You can easily download and share this report with colleagues and candidates.