Data from the real world are never perfectly balanced. In this episode I explain a simple yet effective trick to train models with very unbalanced data. Enjoy the show!

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References

• Leo Breiman, Random Forests, 2001
• C. Chen, A. Liaw, L. Breiman, Using Random Forest to Learn Imbalanced Data (2004)

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References

• https://tapmydata.com/
• https://medium.com/@tholder/we-dont-want-your-data-pushing-boundaries-in-data-collection-and-end-to-end-encryption-for-apps-ebd1d5f79df5

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In this episode I have a really interesting conversation with Karan Grewal, member of the research staff at Numenta where he investigates how biological principles of intelligence can be translated into silicon.
We speak about the thousand brains theory and why neural networks forget.

References

• Main paper on the Thousand Brains Theory: https://www.frontiersin.org/articles/10.3389/fncir.2018.00121/full
• Blog post on Thousand Brains Theory: https://numenta.com/blog/2019/01/16/the-thousand-brains-theory-of-intelligence/
• GLOM paper by Geoff Hinton: https://arxiv.org/pdf/2102.12627.pdf
• Why neural networks forget? https://numenta.com/blog/2021/02/04/why-neural-networks-forget-and-lessons-from-the-brain

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