…and unlock the potential of the best & most underrated Causal Discovery package in Python! — Introduction The recent surge in interest in causality-related topics in Python has led to a wealth of resources making a decision what to focus on challenging. For instance, a popular CausalNex package supported by the US consulting giant McKinsey advertises the NOTEARS algorithm (Zheng et al., 2018) as the “state-of-the-art structure…

…and you can get 3 of them completely for free if you want! 🤗 — Introduction Recent years brought a sharp increase in interest in causal methods in the research community and in the industry. One of the challenges that people entering the field face is a lack of standardized resources and terminology. Causality research has been scattered and divided into sub-fields for decades. …

Learn 3 techniques for causal effect identification and implement them in Python without losing months, weeks or days for research — If you’re reading this you’ve probably been in data science for some 2–5 years now. You have most likely heard about causality before, maybe even read a book or two on the topic, yet if you don’t feel confident or you’re missing some clarity on how to grab these concepts…

A Polish translation of an article, that we wrote with Mike Erlihson, PhD as a part of #DeepNightLearners series. — Scroll down for English and Hebrew versions. Other language versions: - English (original) - Hebrew Współautorem tego artykułu jest Mike Erlihson. W tłumaczeniu pomógł Mateusz Modrzejewski ❤️ W zasadzie dowolne dane mogę być reprezentowane jako wektor w ciągłej przestrzeni reprezentacji. Często jednak wektory dla różnych punktów danych koncentrują się w…

…and you can get them for free if you want! ❤️ — Introduction 2022 was a truly amazing year for the machine learning community worldwide! Many long-awaited titles have been released, including new editions of all-time classics. In this post I want to share with you three 2022 titles that I believe are especially worth reading (not only) this year. At the end…

Part 4: Going fully probabilistic¹ — This series is a brief introduction to modeling uncertainty using TensorFlow Probability library. I wrote it as a supplementary material to my PyData Global 2021 talk on uncertainty estimation in neural networks. Articles in the series: Part 1: An Introduction Part 2: Aleatoric uncertainty Part 3: Epistemic uncertainty Part 4…

Part 3: Epistemic uncertainty — This series is a brief introduction to modeling uncertainty using TensorFlow Probability library. I wrote it as a supplementary material to my PyData Global 2021 talk on uncertainty estimation in neural networks. Articles in the series: Part 1: An Introduction Part 2: Aleatoric uncertainty Part 3: Epistemic uncertainty Part 4…

Part 2: Aleatoric uncertainty — This series is a brief introduction to modeling uncertainty using TensorFlow Probability library. I wrote it as a supplementary material to my PyData Global 2021 talk on uncertainty estimation in neural networks. Articles in the series: Part 1: An Introduction Part 2: Aleatoric uncertainty Part 3: Epistemic uncertainty Part 4…