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Category:
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Bayesian (3)
Mental Models (2)
Probabilistic Programming (5)
Causality (4)
Counterfactual Reasoning (3)
Author:
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Robert Osazuwa Ness
shan
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The Claude Shannon Way of Modeling
Shannon's Communication Theory and its Applications in Data Science
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Causal Modeling in Machine Learning Workshop
The most efficient path to engineering causal reasoning in machine learning applications.
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Causal Generative Machine Learning Minicourse
Become a expert in building learning agents that reason causally
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Probabilistic Machine Learning
The shortest path to building software for Bayesian reasoning and probabilistic machine learning.
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Causal Agents and Reinforcement Learning
Build agents that make decisions using causal reasoning.
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Algorithmizing Counterfactual Reasoning
How to write algorithms that handle advanced causal counterfactuals
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Refactored: A Primer on Inductive Biases
A concise primer on "invariances", "priors" and other "inductive biases" in machine learning and deep neural net architectures.
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