PinnedPublished inTDS Archive🤷 Quantifying Uncertainty — A Data Scientist’s Intro To Information Theory — Part 2/4: EntropyGain intuition into Entropy and master its applications in Machine Learning and Data Analysis. Python code provided. 🐍Feb 34Feb 34
PinnedPublished inTDS Archive😲 Quantifying Surprise — A Data Scientist’s Intro To Information Theory — Part 1/4: FoundationsGain intuition into Information Theory and master its applications in Machine Learning and Data Analysis. Python code provided. 🐍Feb 3Feb 3
PinnedPublished inTDS Archive🚪🚪🐐 Lessons in Decision Making from the Monty Hall ProblemA journey into three intuitions: Common, Bayesian and CausalOct 24, 202412Oct 24, 202412
PinnedPublished inTDS Archive➡️ Start Asking Your Data “Why?” - A Gentle Intro To CausalityBegin your causal journey with visual demonstrations and resources.Sep 12, 20244Sep 12, 20244
PinnedPublished inTDS Archive🧠🧹 Causality - Mental Hygiene for Data ScienceHarness The Power of Why with Causal Tools.Nov 28, 20242Nov 28, 20242
Published inTowards AI🚅 Information Theory for People in a HurryA quick guide to Entropy, Cross-Entropy and KL Divergence. Python code provided. 🐍Mar 76Mar 76
Published inData Science Collective📏 Quantifying Misalignment — A Data Scientist’s Intro To Information Theory — Part 3/4Gain intuition into Cross-Entropy, KL-Divergence and their applications in Machine Learning and Data Analysis. Python code provided. 🐍Feb 24Feb 24
Anatole, thanks for the kind words and for you question!Are you referring to causality or Simpson's Paradox?Oct 24, 2024Oct 24, 2024
Published inTDS Archive🪜 Mastering Simpson’s Paradox — My Gateway to CausalityWarning: You’ll never look at data in the same wayOct 7, 20241Oct 7, 20241
