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This serves as a quick cheat-sheet for solving small Bayesian problems using a table.

Based in Sydney, Australia, Foundry is a blog by Rebecca Thao. Her posts explore modern architecture through photos and quotes by influential architects, engineers, and artists.
The Local Maximum Expand Your Perspective
Episode 64 - Pro Tips for Your Resume with Marc Cenedella
Apr 29

Apr 29 Episode 64 - Pro Tips for Your Resume with Marc Cenedella

Max Sklar
Guest Interview
Episode 63 - Transfer Learning, Career Cliffs, and Investing for the Future with Bethany Crystal of USV
Apr 22

Apr 22 Episode 63 - Transfer Learning, Career Cliffs, and Investing for the Future with Bethany Crystal of USV

Max Sklar
Guest Interview
Episode 62 - Capitalism, Competition, and Wealth: A Conversation with Denise Hearn
Apr 15

Apr 15 Episode 62 - Capitalism, Competition, and Wealth: A Conversation with Denise Hearn

Max Sklar
Guest Interview
Episode 61 - Facebook vs Capitalism
Apr 8

Apr 8 Episode 61 - Facebook vs Capitalism

Max Sklar
Guest Interview
Episode 60 - Hilary Mason, Machine Learning Research in Action
Apr 1

Apr 1 Episode 60 - Hilary Mason, Machine Learning Research in Action

Max Sklar
Guest Interview

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Subscribe on iTunes | Soundcloud | Stitcher | Odysee | Locals

Get Max's Free Cheat-Sheet on Bayesian Inference

Just sign up for the email list to keep up with exciting developments, floating ideas, and new projects from Max and his guests.

Thank you for joining The Local Maximum email list!

Download your free Bayesian Inference Page

This serves as a quick cheat-sheet for solving small Bayesian problems using a table.

The “Local Maximum” is a mathematical term referring to a point at which one needs to step down in order to reach new heights. But people - not just points - can get caught in a local maximum. That's what happens when we've gone as far as we can through one strategy which has gone stale and need to search for new ideas. This podcast is about examining technology, engineering, and social trends by expanding perspectives and moving beyond the Local Maximum both for ourselves and our algorithms.

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