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Training
Course Information
Course Name
GenAI-E: GenAI for Everyone
Duration
3 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
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.
Overview
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.
Audience Profile
Audience Profile
The intended audience for this course:
- Data Engineers
- Data Scientists
- Machine Learning Engineers
- Integration Engineers
- Architects
Prerequisities
Prerequisites
Participants should preferably have some hands-on experience on Python
At Course Completion
At Course Completion
What you’ll learn
- You will be able to discuss the fundamentals of Generative AI or GenAI, some applications of GenAI in daily life, and the responsible use of GenAI.
Course Outline
Course Outline
Module 1: Exploring Generative AI
- Deep Learning Models
- Discriminative vs. Generative AI
- On why does Generative AI matter?
- Content Creation using generative AI
Module 2: 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
Module 3: 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
Module 4: 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
Module 5: 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
Module 6: Ethical Consideration in Generative AI
- Ethical and Proficiency Requirements for Proficient Work in Generative AI
- Reevaluating AI Bias & Human Centrality in Technology
