Duration
2 Days
Overview
This course prepares developers and business analysts 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, business system analysts and more.
Prerequisites
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 AI Models
- OpenAI and ChatGPT
- Applications of Generative AI
Module 2: Variational Autoencoders (VAE)
- What are Variational Autoencoders?
- Architecture and Training
- Applications of VAEs
Module 3 Deep Learning and Generative Adversarial Networks
- What is Deep Learning?
- Basic Components of GANs
- Architecture and Training
- Common GAN Variants and Applications
- VAEs and GANs
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
Module 6 Generative AI in Code Development
- Getting Started with AI Powered Development
- Integration with OpenAI APIs
- Tips and Tricks
- Best Practices
Module 7 Generative AI in Business Analysis
- Getting Started with AI Powered Analysis
- What is Prompt Engineering?
- Tips and Tricks
- Best Practices