How do you stay up to date with the data science and machine learning field? | Hacker News Reader

How do you stay up to date with the data science and machine learning field?

4 pointsmwitiderrick2 comments

How do you stay up to date with what's happening in the data science and machine learning field?

Which newsletter do you follow here are mine.

Deep Learning Weekly https://lnkd.in/d3yR6t27

KDnuggets https://www.kdnuggets.com/

The Variable by Towards Data Science towardsdatascience.com

The Batch by DeepLearning.AI https://lnkd.in/dszi8Jvh

Data Science Weekly https://lnkd.in/dNcuG5zD

PyCoder's Weekly pycoders.com

Neural Magic Latest on ML performance https://lnkd.in/dSEjW2y8

The High ROI Data Science by Vin Vashishta https://lnkd.in/d2S_9W4E

Nick Singh Newsletter https://nicksingh.com/ The Generalization Gap by Mark Tenenholtz https://lnkd.in/dKhKPRrp

Which ones are you subscribed to?

I don't follow anything, the big and important things either bubble up on places like HN/reddit or I'll find them when I need to (if I'm working on a specific problem, I can always look up what the latest / state of the art solutions are).

The last thing I want to do with my time is read data science and ML blogs and newsletters which are guaranteed to be 95%+ crap that's either irrelevant to me, wrong, useless or plagiarized from something I've already read.

>The last thing I want to do with my time is read data science and ML blogs and newsletters which are guaranteed to be 95%+ crap that's either irrelevant to me, wrong, useless or plagiarized from something I've already read.

Are you me? This is exactly how I feel. I've given up on trying to find a good source of Data Science news or articles.

If we're talking about ML models (e.g. Clustering Algorithms and Metrics), once a year I'll try to select a few books published in the last year read them. They'll always be some new ideas and algorithms, and I'll get to refresh my knowledge on existing topics as well.

Throughout the year I'll usually hear (HN/LinkedIn/colleagues) about related books (e.g. Designing Data Intensive Applications) and read them.

That's more than enough to keep me employed and ahead of the game at my job. I'm not a researcher nor a Twitter/LinkedIn "thought leader", so most of what I need to know needs to be tested and validated and not bleeding edge.

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