generative models
AI, ML & GenAI interview questions tagged generative models, across every topic.
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Concepts behind "generative models"
The curriculum that explains the ideas these questions test.
Core
Diffusion ModelsDiffusion models generate images (and audio/video) by learning to reverse a noising process: training corrupts data into noise step by step, and the model learns to denoise, so at generation it begins from pure noise and iteratively denoises into a sample. Text conditioning (via cross-attention to text embeddings) steers what gets generated, and latent diffusion denoises in a compressed space for efficiency. AI, ML, and GenAI interviews probe it because it is the basis of image generation systems and explains their cost, latency, and the role of guidance.🧠 Foundations of LLMs & GenAISign in
Core
Autoencoders and GANsTwo foundational generative architectures: autoencoders squeeze input through a bottleneck and reconstruct it, which makes them useful for denoising, anomaly detection, and learning compact representations, while GANs set a generator against a discriminator in an adversarial game to produce realistic samples. AI, ML, and GenAI engineer interviews probe these because they test whether you grasp the bottleneck principle, the adversarial training dynamics behind mode collapse, and why diffusion models displaced GANs for high-fidelity generation.📊 Evaluation & ML FoundationsSign in