Intro to Generative AI


10 in stock


This course structure  to provide a comprehensive understanding of generative AI, balancing theoretical knowledge with practical applications and ethical considerations.

Here’s a structured approach to cover the essentials:

  1. Introduction to Generative AI

    • Definition and overview of generative AI.
    • Historical context and evolution of the technology.
    • Key concepts and terminology.
  2. Types of Generative AI Technologies

    • Deep Learning and Neural Networks.
    • Generative Adversarial Networks (GANs).
    • Variational Autoencoders (VAEs).
    • Transformer models (like GPT-3, BERT).
    • Case studies and real-world applications.
  3. Ethical Considerations and Challenges

    • Bias in AI models.
    • Ethical use of generative AI.
    • Intellectual property and copyright issues.
    • Privacy concerns.
  4. Practical Applications and Industry Use Cases

    • Creative industries (art, music, writing).
    • Business and marketing (ad generation, product design).
    • Science and research (data analysis, simulation).
    • Healthcare (drug discovery, patient data analysis).
  5. Hands-On Sessions

    • Working with generative AI tools.
    • Building simple models.
    • Analyzing and interpreting AI-generated content.
  6. Future of Generative AI

    • Emerging trends and technologies.
    • Potential impacts on society and industry.
    • The role of AI in shaping future innovations.
  7. Conclusion and Q&A Session

    • Summarize key learnings.
    • Address participant questions.
    • Provide resources for further learning.
  8. Post-Course Activities

    • Assignments or projects to apply learning.
    • Community or forum for ongoing discussion and support.


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