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