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Core
Autoencoders and GANs
Two foundational generative architectures: autoencoders compress input through a bottleneck and reconstruct it, which makes them useful for denoising, anomaly detection, and learning compact representations, while GANs pit a generator against a discriminator in an adversarial game to produce realistic samples. Applied AI interviews probe these because they test whether you understand the bottleneck principle, the adversarial training dynamics that cause mode collapse, and why diffusion models displaced GANs for high-fidelity generation.
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Machine Learning & Data ScienceHow do GANs work, why is training unstable, and why did diffusion overtake them?→Machine Learning & Data ScienceWhat are autoencoders and VAEs, and what is the reparameterization trick?→Machine Learning & Data ScienceYour generative image model produces low-diversity or garbled samples. How do you diagnose and fix it?→Machine Learning & Data ScienceHow do diffusion models work, and what do the VAE and U-Net do in latent diffusion (Stable Diffusion)?→Machine Learning & Data ScienceWhat is self-supervised learning, and how do contrastive methods and masked prediction work?→System Design for AI in ProductionDesign an audio denoising / speech enhancement system.→
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