Introduction to Generative AI


  1. Introduction to Generative AI
    • Definition and scope of generative artificial intelligence
    • Applications and significance in various industries
    • Overview of generative models
  2. Foundations of Generative Models
    • Basics of probability and statistical modeling
    • Overview of generative and discriminative models
    • Understanding the generative process
  3. Probabilistic Graphical Models
    • Introduction to probabilistic graphical models
    • Bayesian networks and Markov random fields
    • Inference in graphical models
  4. Introduction to Generative Adversarial Networks (GANs)
    • Overview of GAN architecture
    • Training process and adversarial training
    • Use cases and applications of GANs
  5. Variational Autoencoders (VAEs)
    • Understanding autoencoders
    • Variational inference and autoencoder variations
    • Applications of VAEs in generative modeling
  6. Recurrent Neural Networks (RNNs) for Generative Sequences
    • Basics of recurrent neural networks
    • Generating sequences with RNNs
    • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks
  7. Transformers and Attention Mechanisms
    • Introduction to transformer models
    • Attention mechanisms and self-attention
    • Applications in natural language processing and image generation
  8. Generative AI in Natural Language Processing
    • Language modeling with generative models
    • Text generation with GPT (Generative Pre-trained Transformer) models
    • Conditional language generation
  9. Generative AI in Computer Vision
    • Image generation with GANs
    • Style transfer and image-to-image translation
    • Generative models for image synthesis
  10. Generative AI in Creative Arts
    • AI-generated art and music
    • Creative applications of generative models
    • Ethical considerations in generative AI art
  11. Evaluation Metrics for Generative Models
    • Metrics for assessing the quality of generated samples
    • Challenges in evaluating generative models
    • Comparative analysis of evaluation methods
  12. Transfer Learning and Fine-Tuning in Generative AI
    • Pre-training and fine-tuning of generative models
    • Transfer learning strategies
    • Adapting pre-trained models to specific tasks
  13. Generative AI and Reinforcement Learning
    • Integrating generative models with reinforcement learning
    • Applications in game playing and decision-making
    • Challenges and opportunities in combined approaches
  14. Ethical Considerations in Generative AI
    • Biases and ethical challenges in generative models
    • Responsible AI practices in generative AI
    • Ensuring fairness and transparency
  15. Future Trends in Generative AI
    • Emerging technologies and advancements
    • Challenges and opportunities in the field
    • Potential directions for future research