##### Scenario Interview on classical machine-learning fundamentals and computer-vision–related techniques. ##### Question Differentiate overfitting and underfitting. How do you detect and mitigate each? What is data augmentation? Provide image-specific examples. Describe the main components and purposes of a Convolutional Neural Network. How does an RNN process sequential data? Detail the roles of positional embeddings, self-attention, residual connections and feed-forward networks in a transformer encoder. What is dropout and why does it help? Compare bagging and boosting in terms of bias, variance and algorithm behavior. ##### Hints Define concepts, mention bias-variance trade-off, regularization tricks, architectures, and practical diagnostics.