Generative AI

Generative AI

10 Weeks
4 Projects
Advanced
$1,499

Course Overview

Explore the cutting-edge field of generative AI and learn to create AI systems that can generate new content

The Generative AI course is designed for advanced learners who want to explore the frontier of artificial intelligence. This comprehensive program covers the latest techniques and models in generative AI, from GANs to transformers and diffusion models.

You'll learn how to build and train AI systems that can generate text, images, audio, and more. Through hands-on projects, you'll develop the skills to create cutting-edge generative models and apply them to real-world problems.

By the end of this course, you'll have a deep understanding of generative AI principles and techniques, enabling you to create innovative AI systems that can generate new content across various domains.

What You'll Learn

Key topics covered in this course

Generative Adversarial Networks (GANs)
Transformer Models
Large Language Models
Diffusion Models
Text-to-Image Generation
Fine-tuning Techniques
Ethical Considerations

Course Content & Information

Detailed curriculum and request more information

Generative AI

Module 1: Introduction to Generative AI

  • What is Generative AI?
  • History and Evolution of Generative Models
  • Applications and Use Cases
  • Setting Up Your Development Environment
  • Project: Simple Generative Model

Module 2: Generative Adversarial Networks (GANs)

  • GAN Architecture and Theory
  • Training GANs
  • Common Challenges and Solutions
  • Advanced GAN Architectures (StyleGAN, CycleGAN)
  • Project: Image Generation with GANs

Module 3: Transformer Models

  • Attention Mechanisms
  • Transformer Architecture
  • Self-Attention and Multi-Head Attention
  • Encoder-Decoder Models
  • Project: Text Generation with Transformers

Module 4: Large Language Models

  • Introduction to LLMs
  • Pre-training and Fine-tuning
  • Prompt Engineering
  • Working with APIs (OpenAI, Hugging Face)
  • Project: Building a Conversational AI

Module 5: Diffusion Models

  • Diffusion Model Theory
  • Denoising Diffusion Probabilistic Models
  • Training Diffusion Models
  • Conditional Generation
  • Project: Image Generation with Diffusion Models

Module 6: Text-to-Image Generation

  • Text-to-Image Architectures
  • CLIP and Contrastive Learning
  • Stable Diffusion
  • Controlling Generation with Prompts
  • Project: Building a Text-to-Image System

Module 7: Ethics and Responsible AI

  • Ethical Considerations in Generative AI
  • Bias and Fairness
  • Deepfakes and Misinformation
  • Responsible AI Development
  • Project: Implementing Ethical Safeguards
Generative AI Projects

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