Basics
Introduction to Generative AI
- What is Generative AI?
- History and evolution of Generative AI
- Key concepts and terminology
Fundamentals of Machine Learning
- Basic principles of machine learning
- Supervised vs. unsupervised learning
- Common algorithms and models
Basic Neural Networks
- Introduction to neural networks
- Structure of a neural network
- Training and evaluating neural networks
Intermediate
Deep Learning Techniques
- Introduction to deep learning
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Generative adversarial networks (GANs)
Natural Language Processing
- Basics of NLP
- Text preprocessing techniques
- Language models and embeddings
- Sequence-to-sequence models
Generative Models
- Understanding generative models
- Variational autoencoders (VAEs)
- Transformer models
- Training and fine-tuning generative models
Data Handling and Preparation
- Data collection and preprocessing
- Data augmentation techniques
- Handling large datasets
- Ethical considerations in data handling
Advanced
Advanced Generative Techniques
- Advanced GAN techniques
- State-of-the-art transformer models
- Hybrid models and architectures
- Real-time generative applications
Optimization and Performance
- Model optimization techniques
- Efficient training methods
- Model compression and quantization
- Performance evaluation and benchmarking
Deployment and Scalability
- Deploying generative models
- Scaling models for production
- Cloud services for AI deployment
- Monitoring and maintaining AI systems
Future Directions
- Emerging trends in generative AI
- Integration with other technologies
- Ethics and societal impact
- Future research directions