Duration

Generative AI for non-IT and IT

3 days

Overview

This course provides an overview into the world of generative artificial intelligence (AI) and the use of various generative models in numerous use cases. As a participant, you'll explore the key concepts, techniques, and best practices involved in working with generative models such as GAN, CLIP, Stable Diffusion, ChatGPT, and Bard.

Outline

Exploring Generative AI
Deep Learning Models
Discriminative vs. Generative AI
On why does Generative AI matter?
Content Creation using generative AI
Exploring Generative Adversarial Networks Approach and Techniques
Image Generation Applications
Understanding GAN Architectures
Data Augmentation with a GAN
Conditional GAN (cGAN)
3D-Object Generation
Style GAN for face generation
Fine Tuning GAN for your custom tasks
Contrastive Language-Image Pretraining (CLIP) Approach and Techniques
From GANs to CLIP
Image To Text Applications
CLIP Architecture
Converting image to Text using CLIP
Fine Tuning CLIP for your custom tasks
Exploring Stable Diffusion Approach and Techniques
Text-to-Image Applications
Image-to-Image Applications
Stable Diffusion Architecture
Stable Diffusion for Text-to-Image generation
Data Augmentation with Stable Diffusion
Stable Diffusion for face generation
Fine Tuning Stable Diffusion for your custom tasks
Generative Language Models
Applications of Natural Language Models in Generative AI
Explore Generative LLMs (Large Language Models)
Explore Google Bard: Features & Capabilities
Explore ChatGPT: Features & Capabilities
Prompt Engineering
Fine Tuning generative models
Ethical Consideration in Generative AI
Ethical and Proficiency Requirements for Proficient Work in Generative AI
Reevaluating AI Bias & Human Centrality in Technology

Programme scope, delivery arrangements and suitability are confirmed during enquiry.