More Than Thirty Machine Learning Blogs and Newsletters that Increased our Productivity.

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More Than Thirty Machine Learning Blogs and Newsletters that Increased our Productivity.

These are blogs, sites, and newsletters that my colleagues and I read. These are good summations that guide us both from Machine Learning research to production.

Photo by Luke Tanis on Unsplash

Research Paper Tools

I think many researchers use arxiv Sanity Preserver, but it is talked about rarely. I use arxiv Sanity Preserver solely to find articles of interest. I use Mendeley to store, read, and markup PDF-formatted articles that I find interesting through arxiv Sanity Preserver.


Over the last eight years, a newer phenomenon has been the uprising of blog sites that are great supplements to arxiv. The following are my “goto” blog sites:

  • Google has many excellent blogs for learning computer science topics:
  1. TensorFlow blog currently has TensorFlow Core, TensorFlow js (javascript), TensorFlow Lite, TFX, and community.
  2. AI & Machine Learning.
  3. Kubernetes Blog is independent of Google now but is a great resource for all things Kubernetes.
  • Medium is a myriad of blogs divided by class or category online daily publications. It is one of our “goto” blog sites. Also, Medium publications such as towardsdatascience, theStartup, machine-learning, Artificial Intelligence, and programming explain and summarize techniques and papers and expose us to what is new or a leading trend.
  • Distill is probably my favorite blog site. What draws me is the clarity of the topic under discussion. The second draw is illuminating graphics. There is an abundance of interactive graphics, probably done with hand-crafted JavaScript. I feel compelled to say; you can get close to these visual effects with the latest releases of HiPlot and Streamlit, both of which are covered in later blogs.
  • CMU Machine Learning blog. The latest research, mainly robotics, at CMU. All other major institutions AI research blogs are found, such as MIT, Stanford, Toronto, U. of Washington, Harvard, and Cambridge (ARM), to name drop a few. Also, there are many blog sites all over the world, namely E.U. countries and China. However, some of the best blog articles are in the native languages such as Mandarin, Cantonese, French, or German.
  • The blog site is a site that lists all the courses produced by, a growing list of tutorials, the “Pie & AI” signup, and “Pie & AI “ event descriptions and dates. I recommend that you go to the site at least once a month to review the new material.
  • The Official NVIDIA Blog is their topic portal to Deep Learning, Developer, Gaming, Artificial Intelligence, etc.


Machine Learning Production topics. Source:
  • site references blog, book, package, community chat group, and three years of courses ranging from beginner to SOTA (state-of-the-art) on deep learning. The package and courses are based on Pytorch, except for the first course based on Tensorflow. I expect another course version next year and another release of the package. Last year they took a stab at Tensorflow on Swift. It will not surprise me if the upcoming course introduces Julia.
  • realpython has the best Python tutorials I have ever read on advanced Python fundamentals. People that are Python beginners, as well as multi-year experienced Python software engineers, can learn from the tutorials on this site.
  • has components (mostly docker images), documentation, tutorials, Jupyter notebooks, code, Tensorflow examples, Kuberflow pipeline example codes, and much more, all centered on Machine Learning. The contents can be used on your local computers and the cloud. It is not specific to the Google Cloud Platform (GCP).
  • Kaggle is the site for Machine Learning competitions, artifacts associated with the kaggle competitions, and various real-world domain datasets in Machine Learning. Many people have used the package to learn machine learning techniques, placing them in the top 10% of any Kaggle competition.
  • PapersWithCode will not stop you from searching GitHub for Python packages but will help you get the source code associated with a published machine learning paper. More critical, this community effort has both TRENDING and SOTA tabs. PapersWithCode is a fantastic compliment to arxiv Sanity Preserver.

The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code, and evaluation tables. —

Other excellent machine learning blogs are:


  • TheSequence Scope is a free subscription, while the Edge has a $50/year subscription fee. The logo and quote state their mission well.
Source: TheSequence blog logo

TheSequence is an unusual way to learn and reinforce your knowledge about machine learning and artificial intelligence.

The Algorithm is a newsletter for people who are curious about the world of AI. I’m here to help you cut through the nonsense and jargon to figure out what truly matters and where all this is headed. You’ll hear from me every Friday with updates and thoughts on the latest AI news and research (as well as some added magic and memes). — Karen Hao, Senior Reporter


These are helpful podcasts, especially if you are in a situation where you can not read.

I find it helpful to print these out. I am old school as you get Python doc snippets with a keystroke in most Python IDEs (Interactive Code Environments — code editors).


Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

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