Harnessing the Power of Generative AI for Intelligent Applications

Overview

Explore the evolution of generative AI, from historical foundations to practical applications in building intelligent chatbots, with insights on tech stacks and future trends.

Syllabus

• Introduction to Generative AI
Overview of AI and Machine Learning
Definition and Scope of Generative AI
Historical Foundations and Key Milestones
• Core Technologies in Generative AI
Neural Networks and Deep Learning
GANs (Generative Adversarial Networks)
Transformers and Large Language Models
Diffusion Models
• Generative AI Techniques
Text Generation
Image and Video Synthesis
Music and Audio Generation
Code Generation
• Building Intelligent Chatbots
Natural Language Processing Essentials
Designing Conversational Interfaces
Integrating Language Models into Chatbots
Case Studies and Real-World Implementations
• Tech Stacks for Developing Generative AI Applications
Popular Frameworks and Libraries (TensorFlow, PyTorch)
Cloud Platforms and Services (AWS, Google Cloud, Microsoft Azure)
Tools for Deployment and Scaling
• Practical Applications of Generative AI
Creative Content Creation
Data Augmentation and Enhancement
Personalization and Recommendation Systems
Automated Programming and Code Assistance
• Ethical Considerations and Challenges
Bias and Fairness in AI
Intellectual Property and Content Ownership
Privacy and Security Concerns
Mitigating Unintended Consequences
• Future Trends in Generative AI
Emerging Research Directions
Potential Impacts on Various Industries
The Role of Generative AI in Digital Transformation
• Course Project
Proposal and Planning
Implementation and Testing
Presentation and Evaluation

Subjects

Computer Science