Welcome to Part 2 of our Deep Learning Interview Questions Series. This edition dives deeper into advanced architectures, training strategies and emerging topics in deep learning. From transformers and attention mechanisms to generative models like GANs and VAEs, these interview questions are curated to reflect modern AI industry demands.
If you’re preparing for roles in cutting-edge AI labs or product-focused teams working with LLMs, computer vision or generative models, these questions and answers will sharpen your technical narrative and decision-making skills.
11. What is the Attention Mechanism in Deep Learning?
The attention mechanism allows models to focus on the most relevant parts of the input when making predictions. Originally introduced in sequence-to-sequence models for machine translation, it has become foundational in modern architectures.
In mathematical terms, attention scores are computed using query (Q), key (K) and value (V) matrices:
This mechanism dynamically weighs different input tokens based on their contextual relevance.
Applications:
- Machine Translation
- Language Modeling (BERT, GPT)
- Image Captioning
- Speech Recognition
12. What are Transformers?
Transformers are a deep learning architecture introduced in the paper “Attention is All You Need.” They rely entirely on self-attention mechanisms and eliminate recurrence, making them highly parallelizable and efficient for large-scale sequence modeling.
Core components:
- Multi-head Self-Attention
- Positional Encoding
- Feedforward Layers
- Residual Connections + Layer Norm
Transformers are the backbone of models like BERT, GPT, T5 and Vision Transformers (ViT). They outperform RNNs in scalability and long-range dependency modeling.
13. What are Generative Adversarial Networks (GANs)?
GANs are a class of generative models consisting of two neural networks:
- Generator: Tries to create realistic data.
- Discriminator: Tries to distinguish real from fake data.
They are trained in a minimax game, where the generator improves by fooling the discriminator over time.
Applications:
- Image synthesis (StyleGAN)
- Data augmentation
- Deepfakes
- Super-resolution
14. What are Variational Autoencoders (VAEs)?
VAEs are probabilistic generative models that learn a latent space for the input data. Unlike traditional autoencoders, VAEs encode inputs as probability distributions.
Key elements:
- Encoder: Outputs mean and variance.
- Latent Sampling: Samples z from the learned distribution.
- Decoder: Reconstructs data from z.
- Loss: Combines reconstruction loss and KL-divergence.
VAEs are used for:
- Image generation
- Semi-supervised learning
- Representation learning
15. What is Self-Supervised Learning?
Self-supervised learning (SSL) is a training strategy where labels are generated from the data itself through pretext tasks. It bridges the gap between supervised and unsupervised learning.
Examples of SSL tasks:
- BERT: Masked language modeling
- SimCLR: Contrastive learning for vision
- BYOL, DINO: Representation learning without negatives
SSL is critical for training large foundation models without costly manual labeling.
16. What is Contrastive Learning?
Contrastive learning is a self-supervised technique where the model learns by bringing similar examples closer in representation space and pushing dissimilar ones apart.
Core idea:
- Positive pair: Augmented views of the same image
- Negative pair: Views from different images
Popular frameworks: SimCLR, MoCo, CLIP
Contrastive learning has revolutionized vision and multimodal representation learning.
17. What is Fine-Tuning in Deep Learning?
Fine-tuning is the process of adapting a pre-trained model to a new task or domain by continuing training with task-specific data.
Strategies:
- Feature extraction: Freeze early layers, retrain final layers.
- Full fine-tuning: Unfreeze all layers and retrain.
- Adapter tuning / LoRA: Add small task-specific modules.
Fine-tuning enables transfer learning, reduces training cost, and improves performance on domain-specific tasks.
18. What are Residual Networks (ResNets)?
ResNets solve the vanishing gradient problem in deep networks using skip connections, allowing gradients to flow through identity paths.
Formula:
H(x) = F(x) + x
They enable training of very deep networks (50, 101, 152 layers) without degradation in performance.
Used in:
- ImageNet classification
- Feature extractors in vision pipelines
19. What is Layer Normalization?
Layer normalization normalizes inputs across features instead of the batch dimension (as in batch norm). It stabilizes training and is widely used in NLP and transformers.
It is independent of batch size and works well with non-iid data, making it ideal for sequence models.
20. What are Attention Heads in Transformers?
In multi-head self-attention, the model learns multiple attention mechanisms in parallel. Each head focuses on different parts of the sequence.
Benefits:
- Capture multiple dependencies
- Attend to different semantic aspects
- Enhance model expressiveness
The outputs of all heads are concatenated and projected back into the model dimension.
Conclusion
This concludes Part 2 of our Deep Learning Interview Questions Series. We explored the frontier of deep learning with cutting-edge techniques like attention, transformers, GANs, and self-supervised learning. These topics form the core of modern AI systems powering tools like ChatGPT, Stable Diffusion, and AlphaFold.
In the upcoming Part 3, we’ll explore:
- Multimodal learning
- Diffusion models
- Long-context transformers
- Model evaluation and interpretability
Stay ahead of the curve by mastering these topics and positioning yourself as a next-generation AI engineer.
Related Read
Deep Learning Interview Questions – Part 1