AI, data and statistics

Deep Learning interview questions

Neural architectures, optimization and representation learning.

6 questions, each with a worked answer

Why do neural networks need a separate validation set? Fundamentals Concept
What could cause unstable training loss in a deep model? Fundamentals Concept
What does gradient clipping solve and what does it not solve? Intermediate Concept
How would you diagnose exploding or vanishing gradients? Intermediate Scenario
Why does deterministic training not guarantee biological reproducibility? Advanced Scenario
How would you detect data shift before deploying a biological model? Advanced Scenario

About Deep Learning

Deep learning uses neural networks with many layers to learn representations directly from raw data such as images, sequences or molecular graphs. Architectures include convolutional networks, recurrent networks and transformers. Training them involves choices about loss functions, optimisation, regularisation and data.

In biotech they power image analysis, protein structure prediction and sequence models, and they demand large, well-curated data to be reliable.

Fundamentals: 2
Intermediate: 2
Advanced: 2

What these questions test

Develop neural-network experiments: Design and compare architectures, optimization and ablations.

References

Common pitfalls and recommended practices. scikit-learn User Guide. scikit-learn.org/stable/common_pitfalls.html.
docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html.
docs.pytorch.org/docs/stable/notes/randomness.html.

Practice material written by TheBench.Bio and checked against the references above, reviewed October 23, 2026. Written to practise with, not collected from real interviews.