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• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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Takeaways:

• The hype around AI in science often fails to deliver practical results.
• Bayesian deep learning combines the strengths of deep learning and Bayesian statistics.
• Fine-tuning LLMs with Bayesian methods improves prediction calibration.
• There is no single dominant library for Bayesian deep learning yet.
• Real-world applications of Bayesian deep learning exist in various fields.
• Prior knowledge is crucial for the effectiveness of Bayesian deep learning.
• Data efficiency in AI can be enhanced by incorporating prior knowledge.
• Generative AI and Bayesian deep learning can inform each other.
• The complexity of a problem influences the choice between Bayesian and traditional deep learning.
• Meta-learning enhances the efficiency of Bayesian models.
• PAC-Bayesian theory merges Bayesian and frequentist ideas.
• Laplace inference offers a cost-effective approximation.
• Subspace inference can optimize parameter efficiency.
• Bayesian deep learning is crucial for reliable predictions.
• Effective communication of uncertainty is essential.
• Realistic benchmarks are needed for Bayesian methods
• Collaboration and communication in the AI community are vital.

Chapters:

00:00 Introduction to Bayesian Deep Learning

06:12 Vincent's Journey into Machine Learning

12:42 Defining Bayesian Deep Learning

17:23 Current Landscape of Bayesian Libraries

22:02 Real-World Applications of Bayesian Deep Learning

24:29 When to Use Bayesian Deep Learning

29:36 Data Efficient AI and Generative Modeling

31:59 Exploring Generative AI and Meta-Learning

34:19 Understanding Bayesian Deep Learning and Prior Knowledge

39:01 Algorithms for Bayesian Deep Learning Models

43:25 Advancements in Efficient Inference Techniques

49:35 The Future of AI Models and Reliability

52:47 Advice for Aspiring Researchers in AI

56:06 Future Projects and Research Directions

Thank you to my Patrons for making this episode possible!

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