Ace Generative AI Interview : 400+ Expert-Level Q&A Mastery

About this course

Prepare to ace your Generative AI interviews with this comprehensive practice course. This course provides 6 full-length practice tests with over 400 conceptual and scenario-based questions covering the core principles and advanced concepts of Generative AI. Designed to help you understand the underlying mathematical models, practical applications, and industry use cases, this course will strengthen your grasp of key topics and boost your confidence.Through targeted practice, you will enhance your understanding of core generative models, including GANs, VAEs, autoregressive models, and diffusion models, while also tackling real-world challenges in model training, evaluation, and ethical considerations.What You Will Learn:Key concepts and mathematical foundations of Generative AIArchitectural differences and applications of GANs, VAEs, autoregressive models, and diffusion modelsTransformer-based generative models, including GPT and DALL·EBest practices for model training, evaluation, and optimizationEthical implications and responsible AI practicesCourse Structure:1. Overview and Fundamentals of Generative AIDefinition and core concepts of generative models vs. discriminative modelsHistorical background and key milestones (e.g., Boltzmann Machines, VAEs, GANs)Applications: Text, image, audio, synthetic data, and moreKey advantages and challenges (e.g., creativity, bias, computational costs)2. Mathematical and Statistical UnderpinningsProbability distributions and latent variablesBayesian inference basics: Prior, likelihood, posteriorInformation theory concepts: Entropy, KL-Divergence, mutual information3. Core Generative Model Families