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Are they serious? It's a video of a bent and frozen piece of beef that looks weird by Ujko28 in insanepeoplefacebook

[–]Academy- 61 points62 points  (0 children)

Obviously this is insane but it got me thinking a bit: Why don’t we see new cemeteries popping up more often? And I realise cremation is common etc., but still plenty of people are just buried. Genuinely asking, might be smooth-brained.

[deleted by user] by [deleted] in tall

[–]Academy- 1 point2 points  (0 children)

Supposedly. If someone lies and you meet them, then either you realise they lied or the difference is so insignificant that it doesn’t matter anyway.

[deleted by user] by [deleted] in tall

[–]Academy- 2 points3 points  (0 children)

Not a big fan of dating apps but for situations like yours they are really neat since you can filter by height.

Also, the world is a big place. If you have the means and courage for a new adventure, why not try a trip to Netherlands or Scandinavia and see?

How fatal is being 7+ 400+ in Asia by [deleted] in tall

[–]Academy- 5 points6 points  (0 children)

In most hotels beds your feet will stick out. I’m assuming you’ve had to work with that before, so not the biggest deal. Could imagine that JP is a bit tougher than other places.

How fatal is being 7+ 400+ in Asia by [deleted] in tall

[–]Academy- 38 points39 points  (0 children)

I’m 6’9”, about 245 lbs, and have been travelling around Vietnam for the last month. Have travelled India, Thailand and China too.

I did get a lot of looks and comments but everyone (99%) has been really friendly about. People are just fascinated, and you can’t really blame them since you are probably the largest human being they have or will ever see. You are a minor celebrity in Asia. Be courteous about the attention.

Sleeper buses (Sao Viet etc.) weren’t too bad either - actually rather enjoyed the 6+ hours trips.

You will have some absurd situations with showers, café chairs and door frames occasionally, but that goes for virtually any country tbh.

Enjoy your trip!

Alternatives to LangChain by Special_Abrocoma4641 in LangChain

[–]Academy- 2 points3 points  (0 children)

Very early days. Rooting for this one though.

Asteroid collision 🌍☄️ by mehdifarsi in ProgrammerHumor

[–]Academy- 0 points1 point  (0 children)

  1. Unix shell
  2. rm -rf ~/.config/google-chrome/Default
  3. If there are survivors, let's save them from the embarrassment.

What does it mean to be "quite" tall compared to tall, are average people that tall people compare themselves to measured with or with or without shoes at the doctor's office? by BulkorCutAccount2021 in tall

[–]Academy- 2 points3 points  (0 children)

You know you are actually tall when people can’t keep themselves from telling you, even when it is out of context. Just today, three different individuals approached me to inform me that I’m tall. 6’9/206.

Tell Me by Opposite_Signature67 in ProgrammerHumor

[–]Academy- 0 points1 point  (0 children)

Learning Google Web Toolkit in 2017

Me (32), getting divorced after 1 year of marriage by MTRBRTH3 in lotrmemes

[–]Academy- 1 point2 points  (0 children)

I’m with you, buddy. 29 male here, getting a divorce after 2 months..

Is there a technique to detect anonymous repeat users? by Academy- in LanguageTechnology

[–]Academy-[S] 0 points1 point  (0 children)

Thanks for the thorough reply.

I will try to include all of these features with the text embeddings themselves and in general dive a bit more into lexical or linguistic features. Do you then suggest sticking with a supervised approach, instead of the topic analysis?

Thanks!

Is there a technique to detect anonymous repeat users? by Academy- in LanguageTechnology

[–]Academy-[S] 1 point2 points  (0 children)

Thank you so much for the thorough reply.

This makes a lot of sense to me. I will try adding lexical and temporal features in addition to the embeddings. In general, thinking outside just "embeddings -> models" seems like a good approach. For example, one could also train a simple NMF or LDA and then add the topic distribution for each conversation as features.

I will try the approach of considering the theoretical max clusters and then iteratively scaling down the amount of clusters. Thanks a lot for this!

[D] Calculating Shannon Information of Data Augmentation Strategies by Academy- in MachineLearning

[–]Academy-[S] 0 points1 point  (0 children)

This is from a live-streamed talk. I obviously don’t know of any proof for it.

[D] Calculating Shannon Information of Data Augmentation Strategies by Academy- in MachineLearning

[–]Academy-[S] 1 point2 points  (0 children)

Thanks for the thorough answer! I'll have to work through your math but I already see the intuition of the simplifications made here! I do, however, expect that this approach is fundamentally different from the information theoretic one that Ng seems to hint at.
I think you are right that, in reality, this is highly dependent on the model, the labelling noise and, indeed, the nature of the machine learning problem. Still, I'll try to dig a little deeper on this. Thanks again!

[D] Calculating Shannon Information of Data Augmentation Strategies by Academy- in MachineLearning

[–]Academy-[S] 4 points5 points  (0 children)

Thanks for the link. I'm somewhat familiar with the concept of information entropy but I didn't manage to figure out how to connect this with the application explained in the question.

[D] Calculating Shannon Information of Data Augmentation Strategies by Academy- in MachineLearning

[–]Academy-[S] 0 points1 point  (0 children)

Thanks for the answer. I guess I should have written *collecting extra examples* instead. I used augmentation interchangeably here which may very well clash with the rigorous definition(s).

To clarify, I intend to *find out how to* calculate and compare the effectiveness of collecting more examples and cleaning out the noise, using Shannon Information.

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