3Blue1Brown explores perhaps the most important formula in probability: Bayes Theorem.

The study with Steve:

3Blue1Brown explores perhaps the most important formula in probability: Bayes Theorem.

The study with Steve:

Learn about bayesian deep learning and how to apply it to a quantum computer from Alex Pozas-Kerstjens

You’ll hear the term Bayes or Bayesian come up a lot in data science, but this video explores the theory with tennis balls and a table.

Here’s a curated list of articles on Bayesian methods and networks.

- An Introduction to Bayesian Reasoning
- Basics of Bayesian Decision Theory
- How Bayesian Inference Works
- Marketing Insight from Unsupervised Bayesian Belief Networks
- Bayesian Nonparametric Models
- Using Bayesian Kalman Filter to predict positions of moving particles
- Naive Bayes Classification explained with Python code
- Wheel Of Fortune – Bayesian Inference
- Neural Networks from a Bayesian Perspective
- A curated list of resources dedicated to bayesian deep learning
- A quick introduction to PyMC3 and Bayesian models
- Analysis of Perishable Products Sales Using Bayesian Inference
- R and Stan: introduction to Bayesian modeling
- And Monty Hall Went Bayesian…
- Bayesian Probability

The next time someone tells you that math has no practical application, tell them about the solved mysteries mentioned in this video.

In this video, Siraj Raval explores how key reinforcement learning algorithms help explain how the human brain works, specifically through the lens of the neurotransmitter known as ‘dopamine’.

These algorithms have been used to help train everything from autopilot systems for airplanes, to video game bots. TD-Learning, Rescorla-Wagner, Kalman Filters, and Bayesian Learning, all in one go!

Julia Galef outlines the most important principles of thinking like a Bayesian.

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