Contents
- Introduction to Generative AI
- Definition and scope of generative artificial intelligence
- Applications and significance in various industries
- Overview of generative models
- Foundations of Generative Models
- Basics of probability and statistical modeling
- Overview of generative and discriminative models
- Understanding the generative process
- Probabilistic Graphical Models
- Introduction to probabilistic graphical models
- Bayesian networks and Markov random fields
- Inference in graphical models
- Introduction to Generative Adversarial Networks (GANs)
- Overview of GAN architecture
- Training process and adversarial training
- Use cases and applications of GANs
- Variational Autoencoders (VAEs)
- Understanding autoencoders
- Variational inference and autoencoder variations
- Applications of VAEs in generative modeling
- 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
- Transformers and Attention Mechanisms
- Introduction to transformer models
- Attention mechanisms and self-attention
- Applications in natural language processing and image generation
- Generative AI in Natural Language Processing
- Language modeling with generative models
- Text generation with GPT (Generative Pre-trained Transformer) models
- Conditional language generation
- Generative AI in Computer Vision
- Image generation with GANs
- Style transfer and image-to-image translation
- Generative models for image synthesis
- Generative AI in Creative Arts
- AI-generated art and music
- Creative applications of generative models
- Ethical considerations in generative AI art
- Evaluation Metrics for Generative Models
- Metrics for assessing the quality of generated samples
- Challenges in evaluating generative models
- Comparative analysis of evaluation methods
- 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
- Generative AI and Reinforcement Learning
- Integrating generative models with reinforcement learning
- Applications in game playing and decision-making
- Challenges and opportunities in combined approaches
- Ethical Considerations in Generative AI
- Biases and ethical challenges in generative models
- Responsible AI practices in generative AI
- Ensuring fairness and transparency
- Future Trends in Generative AI
- Emerging technologies and advancements
- Challenges and opportunities in the field
- Potential directions for future research
