Generative Models and Architectures Questions

Covers the fundamentals of how generative models are constructed and trained, including types such as variational autoencoders, generative adversarial networks, diffusion models, and large language models. Includes core concepts like attention and the transformer architecture, self supervised training objectives such as next token prediction, tokenization, scaling laws, and differences between generative and discriminative approaches. Also addresses practical techniques for adapting and improving models including fine tuning, transfer learning, prompt engineering, few shot and zero shot learning, inference trade offs, model compression, and deployment considerations such as latency, memory, and cost. Evaluation topics include likelihood based metrics and practical applied evaluation methods for generation quality.