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 AI

• Architectural differences and applications of GANs, VAEs, autoregressive models, and diffusion models

• Transformer-based generative models, including GPT and DALL·E

• Best practices for model training, evaluation, and optimization

• Ethical implications and responsible AI practices

Key concepts and mathematical foundations of Generative AI

Architectural differences and applications of GANs, VAEs, autoregressive models, and diffusion models

Transformer-based generative models, including GPT and DALL·E

Best practices for model training, evaluation, and optimization

Ethical implications and responsible AI practices

Course Structure:

1. Overview and Fundamentals of Generative AI

• Definition and core concepts of generative models vs. discriminative models

• Historical background and key milestones (e.g., Boltzmann Machines, VAEs, GANs)

• Applications: Text, image, audio, synthetic data, and more

• Key advantages and challenges (e.g., creativity, bias, computational costs)

Definition and core concepts of generative models vs. discriminative models

Historical background and key milestones (e.g., Boltzmann Machines, VAEs, GANs)

Applications: Text, image, audio, synthetic data, and more

Key advantages and challenges (e.g., creativity, bias, computational costs)

2. Mathematical and Statistical Underpinnings

• Probability distributions and latent variables

• Bayesian inference basics: Prior, likelihood, posterior

• Information theory concepts: Entropy, KL-Divergence, mutual information

Probability distributions and latent variables

Bayesian inference basics: Prior, likelihood, posterior

Information theory concepts: Entropy, KL-Divergence, mutual information

3. Core Generative Model Families

• GANs: Generator-discriminator architecture, training challenges, variations (DCGAN, WGAN, StyleGAN)

• VAEs: Encoder-decoder architecture, ELBO objective, trade-offs with GANs

• Autoregressive Models: PixelCNN, PixelRNN, direct probability estimation

• Normalizing Flows: Invertible transformations, real-world applications

GANs: Generator-discriminator architecture, training challenges, variations (DCGAN, WGAN, StyleGAN)

VAEs: Encoder-decoder architecture, ELBO objective, trade-offs with GANs

Autoregressive Models: PixelCNN, PixelRNN, direct probability estimation

Normalizing Flows: Invertible transformations, real-world applications

4. Transformer-Based Generative Models

• Self-attention mechanism, encoder-decoder vs. decoder-only models

• LLMs: GPT family (GPT-2, GPT-3, GPT-4) and training strategies

• Text-to-image models: DALL·E, Stable Diffusion, challenges and ethical issues

Self-attention mechanism, encoder-decoder vs. decoder-only models

LLMs: GPT family (GPT-2, GPT-3, GPT-4) and training strategies

Text-to-image models: DALL·E, Stable Diffusion, challenges and ethical issues

5. Training Generative Models

• Data collection and preprocessing for consistent input

• Optimization and loss functions (adversarial loss, reconstruction loss)

• Hardware and software ecosystems (TensorFlow, PyTorch)

• Practical techniques: Hyperparameter tuning, gradient penalty, transfer learning

Data collection and preprocessing for consistent input

Optimization and loss functions (adversarial loss, reconstruction loss)

Hardware and software ecosystems (TensorFlow, PyTorch)

Practical techniques: Hyperparameter tuning, gradient penalty, transfer learning

6. Evaluation and Metrics

• Quantitative Metrics: Inception Score (IS), Fréchet Inception Distance (FID), perplexity

• Qualitative Evaluation: Human perceptual tests, user studies

• Challenges in measuring semantic correctness and creativity

Quantitative Metrics: Inception Score (IS), Fréchet Inception Distance (FID), perplexity

Qualitative Evaluation: Human perceptual tests, user studies

Challenges in measuring semantic correctness and creativity

7. Ethical, Social, and Legal Implications

• Bias in training data and mitigation strategies

• Content authenticity, deepfakes, and watermarking

• Copyright issues and ownership of AI-generated content

• Responsible deployment and transparency frameworks

Bias in training data and mitigation strategies

Content authenticity, deepfakes, and watermarking

Copyright issues and ownership of AI-generated content

Responsible deployment and transparency frameworks

8. Advanced Topics and Latest Research

• Diffusion Models: Denoising diffusion models and applications

• Multimodal AI: Cross-modal retrieval and generation

• Reinforcement Learning for Generative Models: Controlled generation strategies

• Self-Supervised Learning: Contrastive learning, masked autoencoding

• Future Trends: Real-time 3D generation, foundation models

Diffusion Models: Denoising diffusion models and applications

Multimodal AI: Cross-modal retrieval and generation

Reinforcement Learning for Generative Models: Controlled generation strategies

Self-Supervised Learning: Contrastive learning, masked autoencoding

Future Trends: Real-time 3D generation, foundation models

This 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.