Discuss overfitting, contrastive learning, transformers

You are interviewing for an applied scientist role and are asked several theory questions.

1. Overfitting
   • Define overfitting and underfitting in supervised learning.
   • How would you detect overfitting in practice (e.g., using training/validation curves or cross-validation)?
   • Describe several techniques to reduce overfitting (at least 3–4 distinct methods) and explain the intuition behind each.
2. Contrastive learning
   • Explain the high-level idea of contrastive learning for representation learning.
   • What are positive and negative pairs, and how are they constructed in practice (e.g., in vision or NLP)?
   • What kinds of downstream tasks benefit from contrastive pretraining?
3. Contrastive loss
   • Describe a common contrastive loss (for example, InfoNCE / NT-Xent style loss).
   • Intuitively explain how this loss encourages representations of positive pairs to be close and negative pairs to be far apart.
   • You may write a simple formula, but focus on the intuition.
4. Transformers
   • At a high level, describe the architecture of a transformer used for NLP or vision tasks.
   • Explain the role of self-attention and how it differs conceptually from recurrent or convolutional approaches.
   • Briefly describe positional encoding and why it is needed.
   • Mention typical components in a transformer block (e.g., multi-head attention, feedforward layers, residual connections, normalization).