Introduction to Generative AI

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Introduction to Generative AI

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  • Duration : 8 - 96 hours
  • Levels : Beginner, Intermediate and Advanced
  • Lectures : 2 - 24
  • Categories: Artificial Intelligence
  • Tags: AI
Price:$250 - $2500
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Embark on a comprehensive exploration of Generative AI through our "Introduction to Generative AI" course at Intell Edge Technologies. Tailored for beginners and AI enthusiasts alike, this program provides a thorough overview of the principles and applications of Generative AI. Participants will delve into the fundamentals of generative models, including well-known architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Through practical demonstrations and hands-on exercises, attendees will gain insights into how Generative AI is applied in image synthesis, text generation, and creative applications. Join us to understand the potential of Generative AI, its implications across various industries, and its role in shaping innovative solutions within the dynamic field of artificial intelligence.

  • Creative Content
  • Neural Networks
  • Algorithmic Art
  • Text Generation
  • Image Synthesis
  • Innovation in AI

Overview

This curriculum provides a comprehensive introduction to the field of Generative AI, covering fundamental concepts, specific models, applications in various domains, ethical considerations, and future trends. Depending on the audience's prior knowledge and depth of coverage required, additional topics or hands-on exercises can be added to enhance the learning experience. Hands-on projects and case studies are crucial for practical understanding and application of generative models

Module 1: Fundamentals of Generative AI

  • Overview of Artificial Intelligence
  • Introduction to Generative AI
  • Distinction between Generative and Discriminative Models
  • Types of Generative Models: GANs, VAEs, and Autoregressive Models
  • Applications of Generative AI in Various Industries
  • Ethics and Responsible AI in Generative Models

Module 2: Generative Adversarial Networks (GANs)

  • Understanding GAN Architecture: Generator and Discriminator
  • GAN Training Process: Adversarial Learning
  • Examples of Successful GAN Applications
  • Challenges and Considerations in GANs
  • Case Study: Real-world GAN Implementations
  • Future Trends and Developments in GANs

Module 3: Variational Autoencoders (VAEs)

  • Introduction to Variational Autoencoders
  • Probabilistic Models and Latent Space Representation
  • Training VAEs and Sampling from Latent Space
  • Use Cases of VAEs in Image and Data Generation
  • Limitations and Challenges in VAEs
  • Case Studies: Real-world Applications of VAEs

Module 4: Text and Language Generation

  • Overview of Language Models in Generative AI
  • Introduction to OpenAI's GPT (Generative Pre-trained Transformer)
  • Text Generation Techniques and Architectures
  • Use Cases of Generative Text Models
  • Ethical Considerations in Text Generation
  • NLP and Language Generation Future Trends

Module 5: Image and Multimedia Generation

  • Image Generation with GANs
  • Style Transfer Techniques
  • Deepfake Technology: Risks and Applications
  • Generative Models for Video and Audio
  • Responsible Use of Generative Models in Multimedia
  • Innovations in Image and Multimedia Generation

Module 6: Applications and Future Directions

  • Generative AI in Healthcare and Drug Discovery
  • Design and Creativity with Generative Models
  • Real-world Implementations in Entertainment and Gaming
  • Bridging Generative AI with Other Technologies (AR, VR)
  • Research Frontiers and Interdisciplinary Applications
  • Preparing for the Future: Careers and Skill Development in Generative AI

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