Duration
3 Days
Overview
This course prepares developers to harness the power of generated AI as they create their solutions. They can craft dynamic content, produce code and documentation, refine user interfaces, and devise customized recommendations. All leading to highly efficient and powerful applications.
Audience
This course will be useful for: software developers, data scientists, ML engineers, AI researchers and more.
Prerequisites
The course is technical in nature and attendees should have the following skills: Python, basic AI concepts, Machine Learning basics, and data storage and access.
Hands On Environment
The course includes hands-on activities, allowing attendees to reinforce the concepts covered in the course content.
Course Outline
Module 1: Introduction to Generative AI
- What is Generative AI?
- Overview of Generative Models
- Generative AI Techniques
- Applications of Generative AI
Module 2: Variational Autoencoders (VAE)
- What are Variational Autoencoders?
- Architecture and Training
- Applications of VAEs
- VAEs and GANs
Module 3 Deep Learning and Generative Adversarial Networks
- What is Deep Learning?
- Basic Components of GANs
- Architecture and Training
- Common GAN Variants and Applications
Module 4: Natural Language Generation (NLG)
- What is Natural Language Generation?
- Overview of Language Models
- Transformer Architecture and Variants
- Applications of NLG in Generative AI
Module 5: Ethics and Responsible AI
- The Importance of AI Ethics
- Bias in Generative Models
- Responsible AI and Best Practices
- Future Research and Open Problems
Module 6 Multimodal Generative AI
- What is Multimodal AI?
- Text-to-Image Synthesis
- Audio-to-Video Synthesis
- Applications of Multimodal Generative AI
Module 7 Style Transfer and Neural Art
- What is Style Transfer?
- Neural Style Transfer Algorithms
- Applications of Style Transfer in Generative AI
- Limitations and Future Directions
Module 8 Generative AI in the Real World
- Marketing and Advertising
- Entertainment and Gaming
- Healthcare and Life Sciences
- Finance and Economics
Module 9 Building and Deploying Generative AI Models
- Model Selection and Fine-Tuning
- Deployment Strategies
- Monitoring and Maintenance
- Ensuring User Privacy and Security