Fundamentals of Generative AI: Course Completion, Resources & Recommendations
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Listen to “Fundamentals of Generative AI: Course Completion, Resources & Recommendations” on Spreaker.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
- Reading:
- “Deep Learning for the Life Sciences” by Bharath Ramsundar et al. (Applications of generative models)
- Tutorials:
- “Generative AI Use Cases” article by IBM
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
- Reading:
- “Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili (Python basics and machine learning libraries)
- Online Tutorials:
- “Getting Started with TensorFlow”
- “Getting Started with PyTorch”
Lesson 3.2: Building a Simple Generative Model
- Reading:
- Follow practical blog posts on building GANs, e.g. Building GANs using PyTorch
- Online Courses:
- Hands-On Generative Adversarial Networks with Keras
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.