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Top /r/MachineLearning posts, Jan 25-31


Downsides to jobs in machine learning fields, AI learning materials, novel topic modelling techniques and weekly simple question threads are all topics of discussion this week on Reddit /r/MachineLearning.



By Grant Marshall.

Topic Model latent structure This week on /r/MachineLearning, we have some interesting job discussions from current ML professionals, a solid list of AI courses, books, and video lectures, and a thread of simple questions for those of you who’ve always had a nagging question you’d like answered.

1. Can we have a regular "Simple Questions Thread"? +80

This thread is a simple discussion about whether or not to start having a simple questions thread for /r/MachineLearning. It’s interesting to see how people view the Reddit community, and this post lead to our #4 post this week.

2. What do you dislike about your machine learning job? +68

In this post, many different current ML practitioners discuss some of the downsides to their particular jobs. Something that may or may not surprise you is just how common social issues are mentioned, like problems with non-technical staff or overly-optimistic expectations, instead of actual complaints about the subject matter or working conditions.

3. A curated list of AI courses, books, video lectures and papers +45

Here is a wide-ranging list of varied resources on learning AI yourself. What makes this particular list interesting is that it’s hosted on Github, so if there’s a particular resource you find missing, you can go ahead and add it yourself with a pull request!

4. Friday's "Simple Questions Thread" - 20150130 +39

This is the result of the #1 Post this week. It includes questions on many topics from CNNs to image recognition and audio processing. The discussions that arise around these questions can be interesting. Look out for the thread every Friday if you have any questions of your own.

5. High-Reproducibility and High-Accuracy Method for Automated Topic Classification +36

This is a more technical post. The link goes to a paper published in APS X (American Physical Society) of all places and introduces a method of topic modelling that the authors compare favorably with LDA. If you’re interested in topic models, give this paper a careful read.

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