Overview
The world is entering a new era where creativity meets computation—Generative AI is at the heart of this transformation. In this hands-on, 3-day course, participants will explore the foundations, techniques, and real-world applications of GenAI models that are shaping industries, from digital art and design to text generation and beyond.
Through a structured journey across powerful models such as GANs, CLIP, Stable Diffusion, ChatGPT, and Bard, learners will develop both conceptual understanding and practical skills to generate images, text, and hybrid media outputs. They’ll also get hands-on with prompt engineering, fine-tuning models, and exploring the ethical dimensions of AI-generated content.
Whether you are a tech enthusiast, creative professional, or AI practitioner, this course equips you to harness generative AI tools to create, innovate, and lead in your domain
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamental differences between discriminative and generative models.
- Explore and apply various Generative Adversarial Network (GAN) architectures for visual content creation.
- Use CLIP models to link language and vision tasks in meaningful ways.
- Generate highly detailed visuals using Stable Diffusion for text-to-image and image-to-image applications.
- Experiment with Large Language Models (LLMs) such as ChatGPT and Bard for content creation, automation, and interaction.
- Learn prompt engineering strategies to optimize responses and outputs from generative tools.
- Fine-tune pre-trained generative models to address domain-specific challenges.
- Critically evaluate the ethical implications of generative AI, including bias, authorship, and responsible use.
Module 1: Foundations of Generative AI
- Demystifying Deep Learning in GenAI
- Discriminative vs. Generative Models: What Sets Them Apart
- Why Generative AI Matters in the Future of Work and Creativity
- Use Cases: AI in Text, Image, Audio, and Content Creation
Module 2: Creating with GANs – Generative Adversarial Networks
- The Architecture Behind GANs: How They Learn to Create
- Practical Applications: From Image Generation to Data Augmentation
- Exploring Conditional GANs and StyleGAN for Facial Generation
- Building Custom Outputs with GAN Fine-Tuning
- Introduction to 3D Object Generation with GANs
Module 3: Bridging Vision and Language with CLIP
- Evolution: From GAN to CLIP and the Rise of Contrastive Models
- Understanding CLIP Architecture
- Image-to-Text Applications: Describe, Interpret, Classify
- Customizing CLIP for Your Own Tasks: Fine-Tuning Techniques
Module 4: Generating Visuals with Stable Diffusion
- Text-to-Image: Generate Stunning Art with Simple Prompts
- Image-to-Image Translation: Modifying, Enhancing, Reimagining
- Stable Diffusion Deep Dive: Architecture and Use Cases
- Face Generation & Data Augmentation with Stable Diffusion
- Training and Tuning for Domain-Specific Visuals
Module 5: Working with Generative Language Models
- Understanding LLMs and Their Role in GenAI
- Exploring Bard and ChatGPT: Capabilities, Strengths, and Differences
- Prompt Engineering: Getting the Most Out of LLMs
- Fine-Tuning Language Models for Targeted Use Cases
- AI Content Generation in Real-World Scenarios
Module 6: Ethics, Responsibility & Human-Centered AI
- Navigating Bias and Fairness in Generative Systems
- Ensuring Data Privacy and Model Transparency
- The Role of Human Creativity in a Machine-Generated World
- Best Practices for Responsible and Ethical AI Development
Programme scope, delivery arrangements and suitability are confirmed during enquiry.