Deep Learning, Transformers, and Audio ML Concepts

You are interviewing for a machine-learning role focused on sequence and audio modeling (e.g., speech, speaker recognition). Answer the following conceptual and practical questions:

1. Gradient issues : Why can gradients vanish or explode in deep neural networks, especially RNNs?
2. LSTM vs. RNN : Compared with a vanilla RNN, what advantages does LSTM provide?
3. Loss functions : Describe the loss functions you have used in your projects and in what scenarios you used each of them.
4. Transformer architecture : Explain in detail the overall architecture of the Transformer model (encoder, decoder, embeddings, attention, feed-forward layers, residual connections, layer normalization, output head, etc.).
5. Attention types : Why do Transformers use attention mechanisms? In a standard encoder–decoder Transformer, what are the main types of attention? Why is multi-head attention used instead of a single head?
6. Q/K/V and formula : Explain the Q/K/V mechanism and the scaled dot-product attention formula. Why do we divide by √dₖ (the square root of the key dimension) in that formula?
7. Cross-attention sources : In encoder–decoder cross-attention, from where do Q, K, and V come respectively—the encoder or the decoder?
8. Parameters updated : During one training step of a Transformer, which parameters get updated?
9. Parameter count : Roughly how many parameters does the Transformer model you used have, and how can you estimate or calculate that number?
10. Positional embeddings : Why are positional embeddings needed in a Transformer? Why are they usually added elementwise to token embeddings instead of being concatenated to them?
11. LayerNorm vs BatchNorm : Why does the standard Transformer use layer normalization instead of batch normalization? What are the differences between them, especially for sequence models?
12. Audio preprocessing : In audio-related work, what audio preprocessing methods and pipelines have you used (e.g., feature extraction, augmentation)? Which PyTorch or related libraries did you rely on?
13. Classical ML & k-NN : What classical machine-learning algorithms are you familiar with? What is the role of the k (number of neighbors) parameter in k-NN? Are decision trees and random forests examples of bagging or boosting methods?
14. Classification vs logistic regression : What is the relationship between general classification methods and logistic regression? How does logistic regression differ from linear regression?
15. Optimizers & epochs : What optimizers have you used in training deep models, and how do you typically decide the number of training epochs?
16. Model I/O : For one of your sequence or audio models, what was the input representation (e.g., waveform, spectrogram, features) and what was the output (e.g., class labels, embeddings, sequences)?
17. TDNN, ECAPA-TDNN, and metrics : Briefly introduce TDNN and ECAPA-TDNN for speaker or audio modeling. What is the equal error rate (EER)? Give the formulas for precision (P), recall (R), and F1.
18. Time-series preprocessing & error analysis : For time-series data, what preprocessing steps have you applied? How did you perform feature selection? Roughly how large was your dataset? If your model achieves 92% accuracy, what might explain the remaining 8% error?
19. Dimensionality reduction & VAE : What dimensionality-reduction methods do you know? What are the differences between a variational autoencoder (VAE) and a standard autoencoder?
20. Recommender systems : What common recommender-system algorithms do you know?
21. Parts of Transformer used : In your projects, which parts of the Transformer did you actually use—only the encoder, only the decoder, or both?
22. End-to-end systems : What does “end-to-end” training mean in machine learning, especially for speech/audio tasks? How can such a system be implemented?
23. Masks in Transformers : Why does a Transformer need masking? In an encoder–decoder Transformer, which kinds of attention (encoder self-attention, decoder self-attention, encoder–decoder cross-attention) require masks, and for what purpose?
24. LLMs, RAG, and hallucinations : What are large language models and retrieval-augmented generation (RAG)? What is the role of RAG, and why do large language models produce hallucinations?
25. Voiceprint recognition : In speaker verification or voiceprint recognition, what loss functions and evaluation metrics are commonly used? What kinds of hyperparameter tuning and optimization can be done in such systems?
26. Whisper : Introduce the Whisper speech-recognition model and explain how its architecture is similar to or different from a “standard” Transformer.
27. VITS : Introduce the VITS text-to-speech model and briefly describe its main components and training objectives.
28. LSTM vs Transformer in practice : Why can LSTMs sometimes perform better than Transformers on certain tasks? Why is Transformer training often relatively slow?
29. Transformers for time series : What are some recent trends or representative ideas in using Transformers for time-series modeling (e.g., handling long sequences, seasonality, or efficiency)?