Intro to Generative AI
Intro to Generative AI
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Intro to Generative AI
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Introduction to Generative AI
- Definition and overview of generative AI.
- Historical context and evolution of the technology.
- Key concepts and terminology.
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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.
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Ethical Considerations and Challenges
- Bias in AI models.
- Ethical use of generative AI.
- Intellectual property and copyright issues.
- Privacy concerns.
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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).
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Hands-On Sessions
- Working with generative AI tools.
- Building simple models.
- Analyzing and interpreting AI-generated content.
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Future of Generative AI
- Emerging trends and technologies.
- Potential impacts on society and industry.
- The role of AI in shaping future innovations.
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Conclusion and Q&A Session
- Summarize key learnings.
- Address participant questions.
- Provide resources for further learning.
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Post-Course Activities
- Assignments or projects to apply learning.
- Community or forum for ongoing discussion and support.
This course structure aims to provide a comprehensive understanding of generative AI, balancing theoretical knowledge with practical applications and ethical considerations.