Shannon's Communication Theory and its Applications in Data Science

The most efficient path to engineering causal reasoning in machine learning applications.

Become a expert in building learning agents that reason causally

The shortest path to building software for Bayesian reasoning and probabilistic machine learning.

Build agents that make decisions using causal reasoning.

How to write algorithms that handle advanced causal counterfactuals

A concise primer on "invariances", "priors" and other "inductive biases" in machine learning and deep neural net architectures.