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Abstract Blue Light
Certified Generative AI Prompt Engineer

Target Students​

AI developers, content creators, and professionals focusing on generative AI models.

Duration :  32 hours (4 days)
Exam Formats​
 

Multiple-choice

Practical Machine Learning Tasks

Learning Objectives

-Master the art of crafting effective AI prompts.

-Understand the mechanics of generative AI models.

-Develop strategies for optimizing generative AI outputs.

-Implement ethical considerations in generative AI usage.

Exam Options​
 

Online

In-Person

Passing Score​​: 70% 
Exam Codes: CGAPE-501
Exam Duration: 2 hours
Course Outline
​Module 1: Foundations and Advanced Concepts in Generative AI
​

  • Deep Dive into Generative AI Models

  • Overview of Key Generative AI Models: GANs, VAEs, and Transformers

  • Theoretical Underpinnings: Probability, Generative Processes, and Model Architectures

  • Comparative Analysis: Strengths and Weaknesses of Different Generative AI Approaches

  • Understanding the Mathematics Behind Generative AI

  • Mathematical Foundations: Probability Distributions, Latent Spaces, and Objective Functions

  • Optimization Techniques: Gradient Descent, Variational Inference, and Backpropagation

  • Implementing Generative Models from Scratch: A Step-by-Step Guide

Module 2: Building and Training Advanced Generative Models
​

  • Advanced Generative Techniques

  • Deep Generative Models: Understanding and Building GANs, VAEs, and Diffusion Models

  • Training Techniques: Handling Mode Collapse, Convergence Issues, and Stability in GANs

  • Leveraging Transfer Learning for Generative AI: Pre-trained Models and Fine-Tuning

  • Implementing Generative AI for Complex Data

  • Generating High-Resolution Images: Techniques and Challenges

  • Text Generation with Transformers: BERT, GPT, and Beyond

  • Audio and Video Generation: Deep Learning Techniques for Media Creation

Module 3: Scaling and Deploying Generative AI Systems
'

  • Scaling Generative AI Models

  • Strategies for Scaling Models to Handle Large Datasets and High-Resolution Outputs

  • Distributed Training Techniques: Parallelism, Data Sharding, and Model Averaging

  • Efficient Resource Management: Balancing Computational Cost and Model Performance

  • Deployment of Generative AI in Production

  • Building Pipelines for Continuous Integration and Deployment (CI/CD) of Generative Models

  • Real-Time Deployment of Generative AI Systems: Challenges and Best Practices

  • Monitoring and Maintaining Generative AI Models Post-Deployment: Handling Drift and Performance Decay

Module 4: Advanced Applications and Innovation in Generative AI
​

  • Innovative Applications of Generative AI

  • Generative AI in Creative Industries: Art, Music, and Design

  • Synthetic Data Generation: Enhancing Data Diversity and Privacy

  • Generative AI in Healthcare: Drug Discovery, Medical Imaging, and Genomics

  • Driving Innovation with Generative AI

  • Exploring New Frontiers: Generative Adversarial Networks for Research and Development

  • Case Studies: Cutting-Edge Applications of Generative AI in Industry

  • Group Project: Designing a Novel Generative AI Application for a Real-World Problem

Module 5: Ethical, Legal, and Social Implications of Generative AI
​

  • Ethical Considerations in Generative AI

  • Addressing Bias, Fairness, and Transparency in AI-Generated Content

  • The Impact of Generative AI on Society: Disinformation, Deepfakes, and Trust

  • Ethical Frameworks and Guidelines for Responsible AI Development

  • Legal and Regulatory Challenges

  • Intellectual Property Issues in AI-Generated Works

  • Compliance with AI Regulations: GDPR, CCPA, and Beyond

  • Navigating Legal Risks: Liability, Accountability, and Governance in Generative AI

  • The Future of Generative AI and Society

  • Predicting the Evolution of Generative AI Technologies

  • Preparing for the Social and Economic Impact of AI-Driven Automation

  • Roundtable Discussion: Shaping the Future of Generative AI with Ethical Considerations

Module 6: Research and Development in Generative AI
​

  • Exploring the Cutting Edge of Generative AI Research

  • Current Trends in Generative AI Research: From Theory to Application

  • Pioneering New Techniques in Generative Modeling

  • Collaborative Research Project: Exploring an Unsolved Problem in Generative AI

  • Innovation Labs and Workshops

  • Hands-On Sessions: Experimenting with Emerging Generative Techniques

  • Peer Review and Feedback on Research Findings

  • Group Presentation: Proposing a Research-Driven Innovation in Generative AI

Module 7: Practical Implementation, Capstone Project, and Presentation
​

  • Capstone Project: End-to-End Generative AI Solution

  • Project Planning: Defining Objectives, Milestones, and Deliverables

  • Implementation: Building, Training, and Deploying a Generative AI Model for a Complex Task

  • Group Collaboration: Working in Teams to Solve a Real-World Challenge with Generative AI

  • Final Presentations and Peer Review

  • Presenting Capstone Projects: Demonstrations and Results

  • Peer Review and Expert Feedback on Project Outcomes

  • Wrap-Up: Lessons Learned and Future Directions

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