Overview

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 FamiliesGANs: Generator-discriminator architecture, training challenges, variations (DCGAN, WGAN, StyleGAN)VAEs: Encoder-decoder architecture, ELBO objective, trade-offs with GANsAutoregressive Models: PixelCNN, PixelRNN, direct probability estimationNormalizing Flows: Invertible transformations, real-world applications4. Transformer-Based Generative ModelsSelf-attention mechanism, encoder-decoder vs. decoder-only modelsLLMs: GPT family (GPT-2, GPT-3, GPT-4) and training strategiesText-to-image models: DALL·E, Stable Diffusion, challenges and ethical issues5. Training Generative ModelsData collection and preprocessing for consistent inputOptimization and loss functions (adversarial loss, reconstruction loss)Hardware and software ecosystems (TensorFlow, PyTorch)Practical techniques: Hyperparameter tuning, gradient penalty, transfer learning6. Evaluation and MetricsQuantitative Metrics: Inception Score (IS), Fréchet Inception Distance (FID), perplexityQualitative Evaluation: Human perceptual tests, user studiesChallenges in measuring semantic correctness and creativity7. Ethical, Social, and Legal ImplicationsBias in training data and mitigation strategiesContent authenticity, deepfakes, and watermarkingCopyright issues and ownership of AI-generated contentResponsible deployment and transparency frameworks8. Advanced Topics and Latest ResearchDiffusion Models: Denoising diffusion models and applicationsMultimodal AI: Cross-modal retrieval and generationReinforcement Learning for Generative Models: Controlled generation strategiesSelf-Supervised Learning: Contrastive learning, masked autoencodingFuture Trends: Real-time 3D generation, foundation modelsThis course will give you a structured and in-depth understanding of Generative AI, equipping you with the knowledge and confidence to tackle real-world challenges and succeed in technical interviews.

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