Aleksander Molak

Published in Towards Data Science

·Sep 27

Causal Python: 3 Simple Techniques to Jump-Start Your Causal Inference Journey Today

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…

Python

11 min read

May 2

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere — a review (Polish)

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…

Unsupervised Learning

7 min read

Understanding Contrastive Representation Learning through Alignment and Uniformity on the…

Feb 4

Three amazing data science books to read in 2023 (if you won’t manage in 2022)

…and you can get them for free if you want! ❤️ — Introduction 2022 is a truly amazing year for the machine learning community worldwide! Many long-awaited titles have been or will be soon 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…

Machine Learning

5 min read

Three amazing data science books to read in 2023 (if you won’t manage in 2022)

Published in Towards Data Science

·Nov 26, 2021

Modeling uncertainty in neural networks with TensorFlow Probability

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…

Bayesian Machine Learning

5 min read

Modeling uncertainty in neural networks with TensorFlow Probability

Published in Towards Data Science

·Nov 19, 2021

Modeling uncertainty in neural networks with TensorFlow Probability

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…

Python

7 min read

Published in Towards Data Science

·Nov 12, 2021

Modeling uncertainty in neural networks with TensorFlow Probability

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…

Bayesian Machine Learning

8 min read

Published in Towards Data Science

·Nov 3, 2021

Modeling uncertainty in neural networks with TensorFlow Probability

Part 1: An introduction — 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…

Python

6 min read