• Overview of Generative AI and Its Applications
• Basics of Machine Learning and Deep Learning
• Neural Network Architectures
• Activation Functions and Optimizers
• Loss Functions in Generative AI
• Training Neural Networks
• Introduction to AI Tools and Frameworks (TensorFlow, PyTorch)

• CNN Architecture and Applications
• Image Feature Extraction and Classification
• Transfer Learning with CNNs
• RNN Architecture and Sequence Modeling
• Applications of RNNs in Language and Time Series Data
• Long Short-Term Memory (LSTM) Networks
• Practical Projects with CNNs and RNNs

• Introduction to GANs and Adversarial Training
• Generator and Discriminator Networks
• Types of GANs (DCGAN, CycleGAN, StyleGAN)
• Image Generation and Style Transfer
• Applications in Image Synthesis and Art
• Training Techniques and Challenges
• Evaluating GAN Models

• Introduction to Variational Autoencoders
• Latent Space Representation
• Applications of VAEs in Image and Data Generation
• Diffusion Models and Applications
• Implementing VAEs for Image Generation
• Combining VAEs with GANs

Practical Use Cases for VAEs and Diffusion Models

• Overview of Transformer Architecture
• Pretrained Models (GPT, BERT, T5)
• Text Generation and Summarization
• Chatbots and Conversational AI
• Sentiment Analysis and Text Classification
• Fine-Tuning and Transfer Learning in NLP
• Advanced NLP Applications with LLMs

• AI for Image Synthesis and Manipulation
• Generative AI in Music and Art Creation
• Text-to-Image and Text-to-Video Models
• Generative AI for Game Design and Storytelling
• Ethical Considerations in Creative AI
• Exploring AI-Generated Content and Ownership
• Project-Based Application in Art and Design

• Optimizing Model Performance
• Model Compression and Quantization
• Deployment on Cloud Platforms (AWS, Google Cloud, Azure)
• Creating API Endpoints for Generative Models
• Ethical Considerations in Generative AI
• Bias Mitigation and Responsible AI Practices
• Monitoring and Maintaining Deployed Models

• Project Planning and Data Collection
• Model Selection and Training for Generative Applications
• Experimenting with GANs, VAEs, or NLP Models
• Evaluating Model Performance and Output Quality
• Final Project Presentation and Peer Review
• Feedback and Final Enhancements
• Real-World Deployment of Capstone Project