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).