Intended Audience: Beginners

TL;DR

This post lists different sources for staying up-to-date with Machine Learning research/news/advances.

Justification for the post

Right now, Machine Learning is in a state of rapid transformation and bold claims. I live in a constant fear of missing out on “cool” ML research and engineering advances. If this feeling echoes with you too, then this post is for you.

Good tech journalists exist but I believe there are only a handful of them covering AI. There is a lot of AI hyperbole in the mainstream news(from Terminator-esque pictures to dystopian explanations totally disconnected from actual research). I will run you through the ways in which I usually get my ML news from and hopefully you’ll get to know a few new sources, in addition to the ones you already know.

Online Forums

Reddit and the associated problems - People have a love-hate relationship with the subreddit r/machinelearning. I visit this sub at least twice a day. I love it but I also can understand why some people hate it. I honestly think with good moderation, everyone can contribute to and learn from this sub. As soon as some good project/paper is published, I can trust fellow redditors to bring it to r/machinelearning. And some meaningful discussions happen and they do get a lot of people involved - from actual researchers, practitioners to general public. Since this subreddit has people from all walks of Machine Learning, it generally tends to favor/upvote the current hot topics(read, Deep Learning) so sometimes significant Bayesian-leaning and other researches do not get popular or even get listed. So watch out for the inherent bias. This sub has a great involvement from research community - I have seen meaningful discussions/clarifications with leading researchers in the industry and academia. Oh, the AMAs are awesome(check out the column on the right). The yang to this yin is, sometimes people get trolled for no apparent reason.

Hacker news is another similar source to check out, but it has a lot of other domain stuff(and occasional hyperboles too).

r/learnmachinelearning for Beginner-focused content.

There are other LinkedIn and Google Groups, but I do not know much about the pros and cons of them.

Twitter

This probably(and surprisingly) is the best source for channeled information(except on those politically charged days). You can follow the researchers in the domain you like and hope that they’ll bring the relevant news to you through their own researches, tweets and re-tweets. For example, if I am interested in Variational Inference, I would follow Shakir Mohamed and Ferenc Huszár and expand to other researchers based on their replies and retweets.

If you need a starting point, check out these two links Twitter ML, Followed by FastML

Newsletters

If your schedule is packed, usually, then this option will definitely work out for you. These two newsletters bring you a curated weekly updates and I totally recommend both of them.

Import AI Jack Clark is OpenAI’s Strategy and Communications Director and he curates this AI Newsletter.

WildML is curated by Denny Britz. He always keeps it short and sweet.

Arxiv and ML Conference Pages

If you are interested in ML research Arxiv is the place to catch up on recent trends in the field. It should actually be number one on your list if you can spare some time daily. It is a pre-print server(and hence sometimes not peer reviewed or waiting to be peer-reviewed) and attracts most of the latest research. If the sheer number of papers seem overwhelming, Arxiv-sanity maintained by Andrej can be of help to you.

The top machine learning conferences(NIPS, ICML, ICLR, etc,.) have accepted + peer reviewed papers, tutorials, posters and workshops in their official pages.

Blogs/Websites

Though they are not the most recent news out there, I strongly believe that there are quite a few ML Blogs that bridge the gap between a classroom and a research paper. Some papers are being introduced to the ML community with a blog post, in tandem. These posts more-often-than-not have nice explanations and awesome visualizations for better understanding.

These are the ones I follow(or have followed in the past). If you know any other good sources of ML News, please leave them in the comments below. Any corrections/content additions, please feel free to issue a PR in Github.

Thanks!