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

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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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

04:24 Vincent Fortuin’s Journey to Bayesian Deep Learning

11:52 Understanding Bayesian Deep Learning

16:29 Current Landscape of Bayesian Libraries

21:11 Real-World Applications of Bayesian Deep Learning

23:33 When to Use Bayesian Deep Learning

28:22 Data Efficiency in AI and Generative Modeling

30:18 Integrating Bayesian Knowledge into Generative Models

31:44 The Role of Meta-Learning in Bayesian Deep Learning

34:06 Understanding Pack Bayesian Theory

37:55 Algorithms for Bayesian Deep Learning Models