Best Machine Learning Blogs to Follow in 2020

From researchers to students, industry experts, and machine learning (ML) enthusiasts — keeping up with the best and the latest machine learning research is a matter of finding reliable sources of scientific work. While blogs usually update in a more informal and conversational style, we have found that the sources in this list are accurate, resourceful, and reliable sources of machine learning research. Fit for all of those interested in learning more about the scientific field of ML.

Please know that the blogs listed below are by no means ranked or in a particular order. They are all incredible sources of machine learning research. Please let us know in the comments if you know of any other reliable blog sources in machine learning.

Machine Learning Blog, ML@CMU, Carnegie Mellon University

The machine learning blog at Carnegie Mellon University, ML@CMU, provides an accessible, general-audience medium for researchers to communicate research findings, perspectives on the field of machine learning, and various updates, both to experts and the general audience. Posts are from students, postdocs, and faculty at Carnegie Mellon.

Distill

Distill is an academic journal in the area of machine learning. The distinguishing trait of a Distill article is outstanding communication and a dedication to human understanding. Distill articles often, but not always, use interactive media. Most articles (if not all) published at Distill often take 100+ hours for publishing.

Google AI Blog

Google AI conducts research that advances the state-of-the-art in the field. Google AI (or Google.ai) is a division of Google dedicated solely to artificial intelligence. It was announced at Google’s conference I/O 2017 by CEO Sundar Pichai [3]. The Google AI blog has a section specifically for machine learning research.

BAIR Berkeley

The BAIR blog provides an accessible, general-audience medium for researchers to communicate research findings, perspectives on the field, and various updates. Posts are from students, postdocs, and faculty in BAIR, and intends to provide a relevant and timely discussion of research findings and results, both to experts and the general audience.