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Fundamentals of Generative AI: Course Completion, Resources & Recommendations

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Course Title: Fundamentals of Generative AI

  • Further Learning Resources:
  • Recommended reading materials
  • Online courses and tutorials

Here are some recommended resources for each module of your course on the fundamentals of Generative AI, including reading materials, online courses, and tutorials.


Module 1: Introduction to Generative AI

Lesson 1.1: What is Generative AI?

  • Reading:
  • “Generative Deep Learning” by David Foster
  • “The Hundred-Page Machine Learning Book” by Gerard Thomas Mariette (Chapters on generative and discriminative models)
  • Online Courses:
  • Introduction to Machine Learning (Coursera)

Lesson 1.2: Applications of Generative AI


Module 2: Core Concepts and Techniques

Lesson 2.1: Machine Learning Basics

  • Reading:
  • “Pattern Recognition and Machine Learning” by Christopher M. Bishop
  • “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
  • Online Courses:
  • Machine Learning by Stanford University (Coursera)

Lesson 2.2: Generative Models Explained

  • Reading:
  • “Generative Adversarial Networks” by Ian Goodfellow et al. (original paper)
  • “Autoencoding Variational Bayes” by D. P. Kingma and M. Welling (original paper)
  • Online Courses:
  • Deep Learning Specialization (Coursera, includes GANs and VAEs)

Module 3: Implementing Generative AI

Lesson 3.1: Setting Up Your Environment

Lesson 3.2: Building a Simple Generative Model


Module 4: Ethical Considerations and Future Trends

Lesson 4.1: Ethical Implications of Generative AI

  • Reading:
  • “Weapons of Math Destruction” by Cathy O’Neil
  • Articles on AI ethics from the Partnership on AI (available on their website)
  • Online Tutorials:
  • AI Ethics course at MIT

Lesson 4.2: The Future of Generative AI

  • Reading:
  • Various articles on emerging trends in AI from sources like Wired, IEEE Spectrum, and The Verge
  • Online Courses:
  • AI for Everyone by Andrew Ng (covers implications and future trends)

Course Completion

Assessment:

Further Learning Resources:

  • Books:
  • “The Deep Learning Revolution” by Terrence J. Sejnowski
  • “Artificial Intelligence: A Guide to Intelligent Systems” by Michael Negnevitsky
  • Online Platforms:
  • edX
  • Udacity for various AI courses
  • Fast.ai for free courses on deep learning

These resources should give participants the foundational understanding and hands-on experience necessary for a solid grasp of Generative AI.