ML Interview Questions

Explain the architecture and functioning of Generative Adversarial Networks (GANs). Discuss their key components, typical challenges encountered during training, and highlight some recent advancements in GAN technology.

Describe the Transformer architecture in detail, focusing on its key components such as the attention mechanism and positional encoding. Discuss how these components contribute to its success in natural language processing (NLP) tasks and compare it to traditional RNN-based models. How can Transformers be adapted for tasks beyond NLP, such as image processing or time series forecasting?

Explain attention mechanisms in deep learning. Compare different types of attention (additive, multiplicative, self-attention, multi-head attention). How do they work mathematically? What problems do they solve? How are they implemented in modern architectures like transformers?