I’m open-sourcing my generative AI reading list! These are all

Let me know if I missed something.

1. Online Courses:
   • Coursera: "Deep Learning Specialization" by Andrew Ng. This comprehensive course covers deep learning concepts, including generative models.
   • Fast.ai: "Practical Deep Learning for Coders" by Jeremy Howard and Sylvain Gugger. This practical course introduces various deep learning techniques, including generative models.
   • Udacity: "Deep Learning Nanodegree" offers a comprehensive program covering various deep learning topics, including generative models.
2. Books:
   • "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This widely acclaimed book covers various aspects of deep learning, including generative models.
   • "Generative Deep Learning" by David Foster. This book provides an in-depth introduction to generative models and their applications.
   • "Grokking Deep Learning" by Andrew Trask. This beginner-friendly book covers deep learning fundamentals, including generative models.
3. Online Tutorials and Blogs:
   • OpenAI's "Generative Models" tutorial series: OpenAI has released several tutorials on generative models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).
   • Machine Learning Mastery: Jason Brownlee's blog covers a wide range of machine learning topics, including generative models. He provides practical examples and tutorials.
   • Towards Data Science: This popular platform hosts numerous articles on generative AI. You can find tutorials, case studies, and discussions related to various generative models.
4. Research Papers and Conferences:
   • ArXiv: ArXiv is a repository of research papers where you can find the latest developments in generative AI. Look for papers on GANs, VAEs, and other generative models.
   • NeurIPS (Conference on Neural Information Processing Systems) and ICLR (International Conference on Learning Representations) are two major conferences where researchers present their work on generative AI. The conference proceedings are usually freely accessible.
5. GitHub Repositories:
   • GitHub hosts many open-source projects and code repositories related to generative AI. You can explore projects implementing GANs, VAEs, and other generative models, which can provide hands-on learning opportunities.

One of the best places to start is with online courses. Platforms like Coursera, edX, and Udacity offer courses on machine learning and AI. Some courses provide a general overview of AI and machine learning, while others focus specifically on generative AI. These courses often include video lectures, quizzes, and assignments to help you learn and practice the concepts.

Another great resource for learning about generative AI is online communities and forums. Reddit has a number of AI and machine learning subreddits, including r/MachineLearning and r/learnmachinelearning, where you can find discussions, resources, and advice from other learners and experts in the field.

You can also find a wealth of information about generative AI on blogs and websites. Some popular blogs on AI and machine learning include Machine Learning Mastery, KDnuggets, and Google AI Blog. These blogs often feature articles, tutorials, and news about advancements in the field.

Finally, if you prefer learning through books, there are many excellent books on AI and machine learning that cover generative AI. Some popular titles include "Hands-On Generative AI with Python" by Simeon Kostadinov, "Generative Deep Learning" by David Foster, and "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

With so many resources available online, there's never been a better time to learn about generative AI. Whether you prefer video lectures, online communities, blogs, or books, there's something for everyone. Happy learning!